Question:
Which of the following outcomes were demonstrated by ATLAS in comparisons with baseline models according to experimental results?
A. Outperformed BART Scaffold in ROUGE scores for eLife lay summaries through multi-attribute control.
B. Achieved higher human evaluation ratings than BART in factuality and comprehensiveness across both datasets.
C. Surpassed GPT3.5-mdc in readability metrics for PLOS summaries despite lower ROUGE-1 scores.
D. Showed degraded performance when removing background information control in ablation studies compared to full configuration.

Answer: AB

Summarisation performance.
Table 2 presents the performance of ATLAS and baseline models using automatic metrics on the test sets of PLOS and eLife. We include the results for ATLAS under two conditions: 1) one utilizing the average value for each attribute observed in the training data for each summary type (ATLAS); and 2) one using true attribute values obtained from gold standard summaries (ATLASOracle), where ATLASOracle is intended to provide an upper bound of the obtainable performance using our control attributes. For all metrics, it is evident from Table 2 that ATLAS exceeds the performance of all baseline approaches for both eLife lay summaries and abstracts, demonstrating a strong ability to control the technicality of generated text whilst producing high-quality summaries. Interestingly, although the GPT3.5-mdc baseline achieves a slightly stronger all-round performance for PLOS lay summaries, it fails to maintain this for the more “lay" summaries of eLife where ATLAS achieves significantly better performance, indicating that our control attributes can effectively capture these differences. In all cases, ATLAS also achieves scores that are comparable to (and sometimes exceeding) that of ATLASOracle, suggesting that the use of the most frequently observed bin value for control attributes is effective for producing the appropriate characteristics for each summary type.

Ablation study.
To assess the contribution of each attribute to model performance, we conduct an ablation study, evaluating ATLASOracle under different configurations. Table 3 reports the results of this study for abstracts and lay summaries on the combined test sets of PLOS and eLife. The table shows that the removal of control attributes has a significant detrimental effect on performance. Additionally, when only a single attribute is included, the length-based control has the highest ROUGE scores, particularly for lay summaries. This is to be expected, as lay summaries are known to differ significantly in length between PLOS (avg. 175.6 words) and eLife (avg. 347.6 words). When employing attributes in combination, we can see that the addition of content word entropy control and the subsequent addition of background information control have the greatest benefit to performance for ATLAS with 2 and 3 attributes, respectively. Interestingly, no attribute emerges clearly as the least effective as, although readability score control is the only one not included in the 3 attribute model, its inclusion in the single attribute model has clear benefits for lay summary performance. This provides further evidence that, in combination, our control attributes are able to capture the differences between summary types and effectuate them during generation.

Human evaluation.
To provide a comprehensive assessment of the summaries generated, we conducted a human evaluation involving our proposed model ATLAS and the strongest baseline model (BART) using two experts. Specifically, adopting a similar setting to the original that of Goldsack et al. (2022), we take a random sample of 10 articles from the test split of each dataset. Alongside each model-generated lay summary, judges are presented with both the abstract and reference lay summary of the given article. We choose not to provide judges with the full article text in an effort to minimise the complexity of the evaluation and the cognitive burden placed upon them. Using 1-5 Likert scale, the judges are asked to rate the model output based on three criteria: (1) Comprehensiveness: to what extent does the model output contain the information that might be necessary for a non-expert to understand the high-level topic of the article and the significance of the research; (2) Layness: to what extent is the content of the model output comprehensible (or readable) to a non-expert, in terms of both structure and language; (3) Factuality: to what extent is the model generated lay summary factually consistent with the two other provided summaries (i.e. abstract and reference lay summary). Table 4 presents the average ratings from our manual evaluation. We calculate the Cohan Kappa scores to measure inter-rater reliability, where we obtain values of 0.50 and 0.57 for eLife and PLOS, attesting to the reliability of our evaluation. The overall results suggest that our proposed method performs better than the BART baseline in terms of all three criteria on both datasets, attesting to their quality. In terms of layness, the higher layness scores observed in the eLife dataset compared to the PLOS dataset align with the previous analysis for the two datasets from (Goldsack et al., 2022). Moreover, compared to baseline, it is worth noting that our model outputs are judged to produce much more factually correct outputs on both datasets, suggesting our method generates fewer hallucinations.

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 337–345
August 11-16, 2024 ©2024 Association for Computational Linguistics
ATLAS: Improving Lay Summarisation with Attribute-based Control
Zhihao Zhang1, Tomas Goldsack2, Carolina Scarton2, Chenghua Lin3∗
1College of Economics and Management, Beijing University of Technology, China,
2Department of Computer Science, University of Sheffield, UK
3Department of Computer Science, The University of Manchester, UK
zhhzhang@bjut.edu.cn
{tgoldsack1, c.scarton}@sheffield.ac.uk
chenghua.lin@manchester.ac.uk
Abstract
Automatic scientific lay summarisation aims
to produce summaries of scientific articles that
are comprehensible to non-expert audiences.
However, previous work assumes a one-size-
fits-all approach, where the content and style of
the produced summary are entirely dependent
on the data used to train the model. In prac-
tice, audiences with different goals and levels
of expertise will have specific needs, impacting
what content should appear in a lay summary
and how it should be presented. Aiming to
address this disparity, we propose ATLAS, a
novel abstractive summarisation approach that
can control various properties that contribute to
the overall “layness" of the generated summary
using targeted control attributes. We evaluate
ATLAS on a combination of biomedical lay
summarisation datasets, where it outperforms
state-of-the-art baselines using both automatic
and human evaluations. Additional analyses
provided on the discriminatory power and emer-
gent influence of our selected controllable at-
tributes further attest to the effectiveness of our
approach.
1
Introduction
Lay summarisation is defined as producing a sum-
mary of a scientific article that is comprehensible
to non-experts (King et al., 2017). Recent work
has shown that, when compared to technical ab-
stracts, lay summaries typically are more readable
(lexically and syntactically), more abstractive, and
contain more background information, enabling a
non-technical reader to better understand their con-
tents (Luo et al., 2022; Cohen et al., 2021; Goldsack
et al., 2023b). However, the extent to which these
attributes are required within a lay summary de-
pends largely on the specific needs of the reader.
For example, a scientist from a related field will
require less background information to understand
an article’s contents than an entirely non-technical
∗Corresponding author
reader, but they might still require domain-specific
jargon to be simplified or explained. Despite its
obvious benefits, to our knowledge, no work has
yet explored how we can enable such fine-grained
control over comprehensibility-related aspects for
lay summary generation.
In this paper, we propose ATLAS (ATtribute-
controlled LAy Summarization), a novel scientific
summarisation approach that aims to control four
attributes targeting distinct properties contributing
to the overall “layness" of the generated summary,
thus allowing it to cater to the specific needs of dif-
ferent audiences. Although recent attempts at text
simplification and story generation have had suc-
cess influencing the style (Martin et al., 2020; Kong
et al., 2021; Sheang and Saggion, 2021) and content
(Kong et al., 2021; Tang et al., 2024) of generated
text using fine-grained controllable attributes, no
work to our knowledge has explored this for sci-
entific summarisation. Luo et al. (2022) recently
addressed the task of readability-controlled scien-
tific summarisation, however, this is only done at
a binary level, training a model to produce either
a technical or non-technical summary based on a
single control token.
Our approach innovates by enabling a greater de-
gree of controllability through the flexible handling
of multiple attributes, allowing it to produce more
diverse summaries and better address the specific
needs of different audiences. Our results show that
ATLAS outperforms state-of-the-art baselines in
both automatic and human evaluations across three
summary types with varying levels of technicality.
Additional analyses confirm that attribute control
positively influences performance, and suggest the
selected control attributes are able to effectively
capture the difference between technical and non-
technical summaries.
337
2
Methodology
As discussed in §1, ATLAS aims to control four
targeted attributes. We use BART-base as the base
model for ATLAS as it represents the state-of-the-
art benchmark in previous lay summarisation works
(Guo et al., 2021; Goldsack et al., 2022).
Formally, each document x = (x1, x2, ..., xn) of
length n, where xi is the i-th token, is prepended
with a control token sequence l such that x =
(l, x1, x2s, ..., xn). l consists of our four selected
control tokens, each of which targets distinct char-
acteristics of the output summary that contributes
to its overall comprehensibility. We describe each
aspect below:
Length (L)
The length of the output summary
in characters. A more lay audience may require a
longer summary to aid comprehension.
Readability (R)
How easy it is to read the text.
This is measured using the Flesh-Kincaid Grade
Level (FKGL) metric, which estimates the reading
grade level (US) required to understand the gener-
ated text based on the total number of sentences,
words, and syllables present within it.
Background information (BG)
The percentage
of sentences classified as containing primarily back-
ground information. Intuitively, a more lay audi-
ence will require greater levels of background in-
formation to contextualise an article.
Content word entropy (CWE)
The average en-
tropy of content words. We hypothesise that jargon
terms are likely to possess higher entropy values,
thus lower average CWE is likely to be a prop-
erty of more lay text. Since jargon terms are pre-
dominately nouns, we extract noun phrases as con-
tent words using CoreNLP library (Manning et al.,
2014).We then follow Xiao et al. (2020) to calcu-
late I(xi) entropy of a given token xi as the nega-
tive logarithm of its generation probability P(xi),
which is directly extracted from a pre-trained lan-
guage model.
I(xi) = −logP(xi)
(1)
During model training, true attribute values (as
calculated on reference summaries) are used, allow-
ing the model to learn to associate attribute values
with summary properties. For all attributes, values
are discretized into 10 fixed-width bins depending
on their respective range in the train split (from
minimum to maximum observed value), resulting
in 10 unique control tokens for each attribute which
are added to the vocabulary. For each attribute at
test time, we use the most common bin value ob-
served for reference summaries of the training set
as attribute values.
3
Experimental Setup
Data.
We experiment on the biomedical lay sum-
marisation datasets introduced in Goldsack et al.
(2022), eLife (4.8k articles) and PLOS (27.5k arti-
cles), for which target lay summaries have been
shown to contain different levels of “layness".
Specifically, eLife’s lay summaries have been char-
acterized as longer, more readable, and more ab-
stractive than those of PLOS, as well as being em-
pirically observed to be suitable for a more lay audi-
ence. We, therefore, combine both of these datasets,
allowing us to expose ATLAS to a greater variety
of attribute values during training.1 For each article
in the combined dataset, we train our ATLAS to
produce both the technical abstract and lay sum-
mary, using our control attributes to differentiate
between them.
Evaluation.
We employ several automatic met-
rics to evaluate the performance of ATLAS. In line
with common summarisation practice, we calcu-
late ROUGE-1,2, and L variants (Lin, 2004) and
BERTScore (Zhang et al., 2019). We also measure
Dale-Chall Readability Score, a metric that esti-
mates US grade level based on the frequency of
common words.
Baselines.
To enable fair comparison, we rerun
many of the baseline approaches used by Gold-
sack et al. (2022) (which have the abstract included
in the input) on the combined datasets. Specif-
ically, we rerun the Lead-3, Lead-K, and oracle
heuristic baselines; TextRank (Mihalcea and Ta-
rau, 2004), LexRank (Erkan and Radev, 2004),
and HipoRank (Dong et al., 2021) unsupervised
models; and BART and BARTScaffold supervised
models. Here, we use the transformer-based BART
base model (Lewis et al., 2020), which we fine-tune
on our own datasets. BARTScaffold is the recre-
ation of a model from Goldsack et al. (2022) which
is trained using a binary control token (<abs> or
<lay>) to produce either an abstract or lay summary
for an article. This model is equivalent to that pro-
1To combine the datasets, we merge the training and vali-
dation sets. We evaluate on the test sets separately.
338
Figure 1: Visualisation of the density distributions of controllable attribute values for each summary type in the
combined train split.
posed by Luo et al. (2022), the only previous work
on controllable lay summarisation.2
Finally, we include two baselines based on Chat-
GPT (3.5-turbo), so as to compare against an ac-
cessible and widely used method of controlling
text generation (i.e., prompt engineering). Our first
GPT baseline (GPT3.5-zs) uses the following zero-
shot prompts: (i) “Summarize the following article
for an expert audience that is familiar with the tech-
nical aspects of the content” to generate technical
abstracts; (ii) “Summarize the following article for
a non-expert audience that has some familiarity
with the technical aspects of the content” to gener-
ate PLOS lay summaries, and (iii) “Summarize the
following article for a non-expert audience that has
no familiarity with the technical aspects of the con-
tent” to generate eLife lay summaries. Our second
GPT baseline (GPT3.5-mdc) replicates the method
of Turbitt et al. (2023), the best-performing team
of the recent BioLaySumm shared task (Goldsack
et al., 2023a). Based on in-context learning, this
method dynamically selects the maximum num-
ber of input-output examples that fit in the context
window (separated by the simple prompt “Expla-
nation:”) to generate lay summaries based on only
the article abstract.
Implementation Details.
As mentioned in §2,
we employ BART-base as our base model. We
train our ATLAS for a maximum of 5 epochs on a
GeForce GTX-1080Ti GPU, retaining the check-
point with the best average ROUGE-1/2/L score on
the validation set. We set the batch size to 1 and
keep the α scale factor (§2) at the default value of
0.2 from Kong et al. (2021).
For calculating control attributes, we use SciB-
ERT (Beltagy et al., 2019) for entropy calculation,
and we employ a BERT-based sequential classi-
2The original code for Luo et al. (2022) is not yet avail-
able at the time of writing and their results are reported on a
different dataset and thus are not comparable.
Summary type
Precision
Recall
F1
Abstract
0.69
0.75
0.72
eLife-Lay
0.71
0.71
0.71
PLOS-Lay
0.73
0.66
0.71
Table 1: Classifier performance for 3-way classification
between summary types on the combined test set.
fier (Cohan et al., 2019) trained on the PubMed-
RTC dataset (Dernoncourt and Lee, 2017) for back-
ground sentence classification (as described in
Goldsack et al. (2022)). We compute the FKGL
readability score using the textstat package.
4
Experimental Results
Discriminatory ability of control attributes.
To
validate the ability of our controllable attributes to
distinguish between different summary types, we
plot the distribution of attribute values for each type
in Figure 1. The figure suggests that, in combina-
tion, the attributes are able to capture characteristic
differences between summary types, as instances
in which two summary types share a similar distri-
bution for one attribute can typically be separated
by other attributes.3
To further evidence this, we use the training set
to train a simple logistic regression classifier, using
only the attribute values of the reference summaries
as features, to discriminate between reference sum-
mary types. The test set results in Table 1 show that
all summary types are classified with an F1-score
above 0.7, attesting to the discriminatory power of
our control attributes.
Summarisation performance.
Table 2 presents
the performance of ATLAS and baseline models
using automatic metrics on the test sets of PLOS
3E.g., PLOS lay summaries and abstracts have similar
readability distributions but differ in their comprehensibility,
length, and entropy distributions. Similarly, PLOS and eLife
lay summaries have similar comprehensibility distributions
but differ in their readability and length.
339
Model
Abstract
Lay summary - PLOS
Lay summary - eLife
R-1
R-2
R-L
BS
DCRS
FKGL
R-1
R-2
R-L
BS
DCRS
FKGL
R-1
R-2
R-L
BS
DCRS
FKGL
Heuristic
Lead-3
23.86
5.66
21.48
81.17
12.66
14.82
27.41
6.87
24.61
83.36
12.66
15.08
19.41
4.06
18.02
81.65
12.65
13.30
Lead-K
35.69
9.07
32.70
82.86
11.69
14.49
38.28
9.45
34.8
83.72
11.88
14.95
37.27
7.53
35.18
82.05
10.58
11.89
Oracle
60.08
27.48
55.95
87.35
11.12
15.15
57.82
23.92
53.37
87.13
11.20
15.28
48.92
13.42
46.30
82.94
10.51
13.18
Unsup.
TextRank
40.26
11.53
36.02
83.83
11.78
20.08
37.55
8.50
33.28
83.43
11.87
20.27
33.88
5.79
31.55
81.16
11.30
18.98
LexRank
38.22
13.06
35.42
83.85
9.70
14.23
31.20
9.09
28.72
82.97
9.70
14.59
32.25
5.73
30.45
80.67
9.68
13.32
HipoRank
36.95
10.19
33.89
83.22
12.15
14.46
37.67
9.22
34.28
83.68
12.15
14.69
31.50
5.17
29.68
80.88
12.13
12.13
Supervised
BART
43.34
13.14
39.80
85.48
11.33
14.40
43.52
12.09
39.67
85.70
11.29
14.54
31.17
6.74
29.20
83.55
11.15
13.87
BARTScaffold
43.13
12.87
39.66
85.33
11.10
14.14
43.73
12.22
39.92
85.67
11.30
14.58
43.01
10.82
40.54
84.88
9.68
11.85
GPT3.5-zs
28.69
6.52
15.04
82.76
11.70
14.32
42.74
12.70
22.28
86.32
10.40
13.19
33.72
8.45
16.95
84.36
10.36
13.03
GPT3.5-mdc
-
-
-
-
-
-
44.41
14.16
41.12
86.55
10.36
13.32
37.97
9.39
35.57
84.22
10.78
13.70
ATLAS
45.87
14.08
42.32
85.54
10.96
14.21
44.44
12.33
40.60
85.70
11.22
14.58
46.80
12.57
44.14
85.20
8.95
10.87
ATLASOracle
46.11
14.07
42.51
85.69
10.99
14.13
44.97
12.49
41.02
85.82
11.21
14.48
46.61
12.29
43.95
85.11
9.18
11.39
Table 2: Summarization performance on the PLOS and eLife test sets (abstracts combined). R = ROUGE F1 (↑),
BS = BERTScore (↑), DCRS = Dale-Chall Readability Score (↓), FKGL = Flesh-Kincaid Grade Level (↓). For
supervised models, we highlight the best score obtained for each metric in bold and underline second best.
and eLife. We include the results for ATLAS under
two conditions: 1) one utilizing the average value
for each attribute observed in the training data for
each summary type (ATLAS); and 2) one using
true attribute values obtained from gold standard
summaries (ATLASOracle), where ATLASOracle is
intended to provide an upper bound of the obtain-
able performance using our control attributes.
For all metrics, it is evident from Table 2 that
ATLAS exceeds the performance of all baseline
approaches for both eLife lay summaries and ab-
stracts, demonstrating a strong ability to control
the technicality of generated text whilst producing
high-quality summaries. Interestingly, although the
GPT3.5-mdc baseline achieves a slightly stronger
all-round performance for PLOS lay summaries, it
fails to maintain this for the more “lay" summaries
of eLife where ATLAS achieves significantly better
performance, indicating that our control attributes
can effectively capture these differences.
In all cases, ATLAS also achieves scores that are
comparable to (and sometimes exceeding) that of
ATLASOracle, suggesting that the use of the most
frequently observed bin value for control attributes
is effective for producing the appropriate character-
istics for each summary type.
Ablation study.
To assess the contribution of
each attribute to model performance, we conduct
an ablation study, evaluating ATLASOracle under
different configurations.4 Table 3 reports the re-
sults of this study for abstracts and lay summaries
on the combined test sets of PLOS and eLife.
The table shows that the removal of control at-
tributes has a significant detrimental effect on per-
formance. Additionally, when only a single at-
tribute is included, the length-based control has
4We use ATLASOracle as the subject of this experiment
rather than ATLAS to get a true reflection of each attribute’s
influence, rather than an approximation.
Model
Lay summary
Abstract
R-1
R-2
R-L
DCRS
R-1
R-2
R-L
DCRS
BART
41.68
11.29
38.12
11.27
43.34
13.14
39.80
11.33
+R
43.34
12.03
39.75
10.91
43.49
13.23
39.95
11.12
+BG
42.52
11.71
39.01
11.01
43.74
13.65
40.35
10.98
+CWE
41.58
11.21
38.04
11.28
44.23
13.48
40.56
11.35
+L
44.22
12.21
40.55
10.81
44.83
13.75
41.31
11.03
+L+BG
44.66
12.36
40.96
10.99
45.67
13.78
42.02
11.17
+L+R
44.52
12.10
40.73
10.92
45.54
13.64
41.78
11.21
+L+CWE
44.72
12.41
41.04
10.88
45.87
13.99
42.32
10.10
+L+R+BG
44.82
12.41
41.10
10.97
45.94
14.07
42.32
11.10
+L+R+CWE
44.83
12.39
41.05
10.90
45.60
13.63
41.84
11.21
+L+BG+CWE
45.01
12.56
41.38
10.88
46.04
14.16
42.44
11.06
ATLASOracle
45.22
12.47
41.45
10.91
46.11
14.07
42.51
10.99
Table 3: Ablation study on the ROUGE-based perfor-
mance of ATLAS under different configurations using
true attribute values. “+" denotes aspect addition. L =
Length, R = Readability, CWE = Content Word Entropy,
BG = Background information.
the highest ROUGE scores, particularly for lay
summaries. This is to be expected, as lay sum-
maries are known to differ significantly in length
between PLOS (avg. 175.6 words) and eLife (avg.
347.6 words). When employing attributes in com-
bination, we can see that the addition of content
word entropy control and the subsequent addition
of background information control have the great-
est benefit to performance for ATLAS with 2 and 3
attributes, respectively. Interestingly, no attribute
emerges clearly as the least effective as, although
readability score control is the only one not in-
cluded in the 3 attribute model, its inclusion in the
single attribute model has clear benefits for lay sum-
mary performance. This provides further evidence
that, in combination, our control attributes are able
to capture the differences between summary types
and effectuate them during generation.
Human evaluation.
To provide a comprehensive
assessment of the summaries generated, we con-
ducted a human evaluation involving our proposed
model ATLAS and the strongest baseline model
340
Criteria
eLife
PLOS
BART
ATLAS
BART
ATLAS
Comprehensiveness
2.30
2.65
2.00
2.55
Layness
2.60
3.05
2.10
2.45
Factuality
2.20
2.85
2.05
2.40
Table 4: Human evaluation on eLife and PLOS. Mean
evaluator ratings (1-5) obtained by BART and ATLAS
outputs for each metric.
(BART) using two experts.5 Specifically, adopting
a similar setting to the original that of Goldsack
et al. (2022), we take a random sample of 10 ar-
ticles from the test split of each dataset. Along-
side each model-generated lay summary, judges
are presented with both the abstract and reference
lay summary of the given article. We choose not
to provide judges with the full article text in an
effort to minimise the complexity of the evalua-
tion and the cognitive burden placed upon them.
Using 1-5 Likert scale, the judges are asked to
rate the model output based on three criteria: (1)
Comprehensiveness: to what extent does the model
output contain the information that might be neces-
sary for a non-expert to understand the high-level
topic of the article and the significance of the re-
search; (2) Layness: to what extent is the content
of the model output comprehensible (or readable)
to a non-expert, in terms of both structure and lan-
guage; (3) Factuality: to what extent is the model
generated lay summary factually consistent with
the two other provided summaries (i.e. abstract and
reference lay summary).6
Table 4 presents the average ratings from our
manual evaluation. We calculate the Cohan Kappa
scores to measure inter-rater reliability, where we
obtain values of 0.50 and 0.57 for eLife and PLOS,
attesting to the reliability of our evaluation. The
overall results suggest that our proposed method
performs better than the BART baseline in terms of
all three criteria on both datasets, attesting to their
quality. In terms of layness, the higher layness
scores observed in the eLife dataset compared to
the PLOS dataset align with the previous analysis
for the two datasets from (Goldsack et al., 2022).
Moreover, compared to baseline, it is worth noting
that our model outputs are judged to produce much
more factually correct outputs on both datasets, sug-
gesting our method generates fewer hallucinations.
5Both judges have experience in scientific research and
hold at least a bachelor’s degree.
6For example, for the “Layness“ criteria, a score of 5 is
equal to “highly lay" and a score of 1, “highly technical".
Model
FKGL
CLI
DCRS
PLOS
ATLAStechnical
15.11
14.21
11.64
ATLASlay
13.22
13.97
11.22
eLife
ATLAStechnical
14.77
14.02
11.32
ATLASlay
10.89
11.45
9.17
Table 5: Readability metrics for two versions of ATLAS
with highly lay and technical attribute values.
Controllability analysis.
To assess the extent to
which our control attributes enable controllability
over the overall layness of the text, we conduct a
further analysis using two additional versions of
ATLAS with highly lay or technical values. Specif-
ically, we create ATLASlay and ATLAStechnical
by selecting the lowest and highest attribute bins,
respectively, for which there are at least 100 obser-
vations in the training data (for all attributes other
than length which is kept constant).
We examine how these extreme attributes mani-
fest themselves in generated summaries by calculat-
ing the average readability values obtained by the
generated summaries for both datasets. We present
the results of the analysis in Table 5, which show
a significant divergence in the readability values
obtained by each model on both datasets. Inter-
estingly, this divergence is substantially wider for
summaries generated on eLife, the dataset which is
identified by Goldsack et al. (2022) as containing
lay summaries that are more “lay” than those of
PLOS, suggesting that exposure to more extreme
values whilst training on this dataset may enable
even greater controllability at inference time.7
5
Conclusion
In this paper, we introduce ATLAS, a model for
controllable lay summarisation that employs con-
trollable attribute tokens to influence various prop-
erties of the generated summary, enabling it to cater
to users of different levels of expertise. Using com-
bined datasets for biomedical lay summarisation
we perform multiple experiments whereby we con-
firm the ability of our selected control attributes
to discriminate between summary types, demon-
strate their effectiveness for controllable lay sum-
marisation, and further investigate their ability to
effectuate desired differences during generation.
7Examples of summaries generated by these models are
included in the Appendices.
341
Limitations
Although our results demonstrate that our selected
control attributes are able to effectively capture the
characteristics between summary types, it is highly
likely that there are additional attributes that we
have not explored that could benefit performance
for controllable lay summarisation. We plan to
explore this in future work, in addition to experi-
menting with more complex methods for enabling
controllability.
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A
Appendix
ChatGPT Baseline Prompts
The prompts pro-
vided to ChatGPT for each summary type are given
in Table 6. To ensure a fair comparison, we control
the length of the GPT baselines using the genera-
tion arguments, (e.g., max_new_tokens). Note that
we differentiate between the lay summary types
(namely, PLOS and eLife) based on distinctions
made by Goldsack et al. (2022), who recognise
PLOS’ summaries as the less “lay" of the two, mak-
ing them better suited to an audience with some
technical knowledge.
343
Summary Type
Prompt
Abstract
Summarize the following article for an expert audience that is familiar
with the technical aspects of the content
PLOS lay summary
Summarize the following article for a non-expert audience that has some
familiarity with the technical aspects of the content
eLife lay summary
Summarize the following article for a non-expert audience that has no
familiarity with the technical aspects of the content
Table 6: Prompts used for the GPT3.5-zs baseline for each summary type.
eLife
ATLAStechnical
The effects of muscle fatigue on motor learning under fatigue are poorly understood. Here, we investigated the
effect of fatigue on learning under a sequential pinch force task. Irrespective of whether the observed fatigue effects
are domain-specific or present in another task that is cognitive demanding but requires minimal force control, we
found that participants had impaired skill learning in both the fatigued and unfatigued effector. We replicated the
findings of experiment 1 and found that disruption of rTMS to the motor cortex (Cantarero et al ., 2013a) alleviated
the adverse effects of fatigue. Cortical excitability was similar to that observed in the untrained effector, but not in
the unfatigued. Altogether, our findings suggest that motor fatigue has a domain-dependent lasting effect on skill
learning. Future studies should focus on understanding the role of motor cortex excitability in the acquisition of
motor skills under fatigue, as well as the potential role for maladaptive memory formation under fatigued conditions.
Cortical and motor cortices should be included in training and rehabilitation regimens geared to improve motor skill
acquisition.
ATLASlay
Muscle fatigue is a neuromuscular phenomenon that can impair performance over time. People who experience
fatigue tend to be less able to learn a new motor skill than people who experience no fatigue. However, it is not
clear how fatigue affects the ability of people to learn new motor skills . One way to study the effects of fatigue is
to study how people learn a motor skill under fatigue conditions. One of the main challenges in studying motor
learning under fatigue is the so-termed “performance-learning" distinction In this study, participants were asked
to practice a motor task over two days and then had to catch up to the skill performance level of the non-fatigued
group. Unexpectedly , participants who were only fatigued at the end of the training were less likely to learn the
motor skill. This suggests that fatigue has a domain-specific lasting effect on the learning of a skill. ernas et al. now
show that people who are unable to recover the motor task under fatigue are more likely to be unable to learn their
motor skill when they are not fatigued. The experiments show that when people are trained to perform the task,
their ability to recover from fatigue is severely impaired. This effect is due to a change in the strength of the motor
cortex, a region of the brain that is involved in learning and memory.
Figure 2: An case study from the eLife test set comparing summaries generated under highly lay and technical
attribute values (with the length attribute being kept constant).
344
PLOS
ATLAStechnical
In this paper, we explore the conditions under which associations between antigenic, metabolic and virulence
properties of strains within pneumococcal populations and predict how these may shift under vaccination. In this
work , we use a conceptual framework to investigate the dynamics of associations between serotype, serotype
and serotype-specific immunity in pneumococcus populations. We find that antigenic type (AT) is the principal
determinant of non-capsular virulence factors (VF) , whereas MT is the major determinant. AT and MT are highly
non-random; MT and AT are co-evolved and co-expressed. ET and CT are also found to be highly correlated,
suggesting that they have synergistically adapted to a particular metabolic niche. IT and LD are found to have
similar patterns of linkage disequilibrium (LD) than randomly selected genes not associated with metabolic/transport
processes; AT is associated with a higher frequency of LD LD than MT LD; CT LD=0.013). CT is the first
mathematical model to explain the non-overlapping association between serotypic and serotypes. TCT BC LD
is a useful tool for predicting the potential impact of vaccination on the prevalence of serotypes associated with
non-vaccine serotypes and for predicting how they may change under vaccination and vaccine serotype replacement.
ATLASlay
Pneumococcal populations are highly diverse in non-antigenic genes and are commonly classified into sequence
types (ST) by Multi Locus Sequence Typing (MLST) of seven metabolic housekeeping genes. STs have been
documented to occur regularly throughout the past 7 decades, yet many studies (eg) show an intriguing pattern of
largely non-overlapping associations between serotype and ST. It has been noted that many STs that were previously
associated with vaccine serotypes now occur in association with non-vaccine serotypes. It has been proposed that a
combination of immune-mediated interference between identical antigenic types and direct competition between
identical metabolic types can generate non-overlapping association between antigenic and STs in populations of the
bacterial pathogen Neisseria meningitidis . In this paper, we explore whether pneumococcal population structure,
can be explained within a similar conceptual framework. in which pathogen strains are profiled by antigenic type,
AT, metabolic type (MT) and additional non-capsular virulence factors (VF).
Figure 3: An case study from the eLife test set comparing summaries generated under highly lay and technical
attribute values (with the length attribute being kept constant).
345


Question:
Which of the following conclusions are supported by the human evaluation and baseline comparisons in the paper?
A. ATLAS generates summaries with higher comprehensiveness scores than BART due to its ability to incorporate domain-specific jargon without simplification.
B. GPT3.5-mdc outperforms ATLAS on PLOS lay summaries because it dynamically selects input-output examples based on the article’s abstract.
C. ATLAS achieves higher layness and factuality scores than BART across both eLife and PLOS datasets, indicating better alignment with non-expert needs.
D. The Dale-Chall Readability Score for ATLAS-generated summaries is inversely correlated with the inclusion of Background Information control tokens.

Answer: BC

Summarisation performance.
Table 2 presents the performance of ATLAS and baseline models using automatic metrics on the test sets of PLOS and eLife. We include the results for ATLAS under two conditions: 1) one utilizing the average value for each attribute observed in the training data for each summary type (ATLAS); and 2) one using true attribute values obtained from gold standard summaries (ATLASOracle), where ATLASOracle is intended to provide an upper bound of the obtainable performance using our control attributes.
For all metrics, it is evident from Table 2 that ATLAS exceeds the performance of all baseline approaches for both eLife lay summaries and abstracts, demonstrating a strong ability to control the technicality of generated text whilst producing high-quality summaries. Interestingly, although the GPT3.5-mdc baseline achieves a slightly stronger all-round performance for PLOS lay summaries, it fails to maintain this for the more “lay" summaries of eLife where ATLAS achieves significantly better performance, indicating that our control attributes can effectively capture these differences.
In all cases, ATLAS also achieves scores that are comparable to (and sometimes exceeding) that of ATLASOracle, suggesting that the use of the most frequently observed bin value for control attributes is effective for producing the appropriate characteristics for each summary type.

Human evaluation.
To provide a comprehensive assessment of the summaries generated, we conducted a human evaluation involving our proposed model ATLAS and the strongest baseline model (BART) using two experts. Specifically, adopting a similar setting to the original that of Goldsack et al. (2022), we take a random sample of 10 articles from the test split of each dataset. Alongside each model-generated lay summary, judges are presented with both the abstract and reference lay summary of the given article. We choose not to provide judges with the full article text in an effort to minimise the complexity of the evaluation and the cognitive burden placed upon them. Using 1-5 Likert scale, the judges are asked to rate the model output based on three criteria: (1) Comprehensiveness: to what extent does the model output contain the information that might be necessary for a non-expert to understand the high-level topic of the article and the significance of the research; (2) Layness: to what extent is the content of the model output comprehensible (or readable) to a non-expert, in terms of both structure and language; (3) Factuality: to what extent is the model generated lay summary factually consistent with the two other provided summaries (i.e. abstract and reference lay summary).

Table 4: Human evaluation on eLife and PLOS. Mean evaluator ratings (1-5) obtained by BART and ATLAS outputs for each metric.
Criteria
eLife
PLOS
BART
ATLAS
BART
ATLAS
Comprehensiveness
2.30
2.65
2.00
2.55
Layness
2.60
3.05
2.10
2.45
Factuality
2.20
2.85
2.05
2.40

Table 4 presents the average ratings from our manual evaluation. We calculate the Cohan Kappa scores to measure inter-rater reliability, where we obtain values of 0.50 and 0.57 for eLife and PLOS, attesting to the reliability of our evaluation. The overall results suggest that our proposed method performs better than the BART baseline in terms of all three criteria on both datasets, attesting to their quality. In terms of layness, the higher layness scores observed in the eLife dataset compared to the PLOS dataset align with the previous analysis for the two datasets from (Goldsack et al., 2022). Moreover, compared to baseline, it is worth noting that our model outputs are judged to produce much more factually correct outputs on both datasets, suggesting our method generates fewer hallucinations.

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 337–345
August 11-16, 2024 ©2024 Association for Computational Linguistics
ATLAS: Improving Lay Summarisation with Attribute-based Control
Zhihao Zhang1, Tomas Goldsack2, Carolina Scarton2, Chenghua Lin3∗
1College of Economics and Management, Beijing University of Technology, China,
2Department of Computer Science, University of Sheffield, UK
3Department of Computer Science, The University of Manchester, UK
zhhzhang@bjut.edu.cn
{tgoldsack1, c.scarton}@sheffield.ac.uk
chenghua.lin@manchester.ac.uk
Abstract
Automatic scientific lay summarisation aims
to produce summaries of scientific articles that
are comprehensible to non-expert audiences.
However, previous work assumes a one-size-
fits-all approach, where the content and style of
the produced summary are entirely dependent
on the data used to train the model. In prac-
tice, audiences with different goals and levels
of expertise will have specific needs, impacting
what content should appear in a lay summary
and how it should be presented. Aiming to
address this disparity, we propose ATLAS, a
novel abstractive summarisation approach that
can control various properties that contribute to
the overall “layness" of the generated summary
using targeted control attributes. We evaluate
ATLAS on a combination of biomedical lay
summarisation datasets, where it outperforms
state-of-the-art baselines using both automatic
and human evaluations. Additional analyses
provided on the discriminatory power and emer-
gent influence of our selected controllable at-
tributes further attest to the effectiveness of our
approach.
1
Introduction
Lay summarisation is defined as producing a sum-
mary of a scientific article that is comprehensible
to non-experts (King et al., 2017). Recent work
has shown that, when compared to technical ab-
stracts, lay summaries typically are more readable
(lexically and syntactically), more abstractive, and
contain more background information, enabling a
non-technical reader to better understand their con-
tents (Luo et al., 2022; Cohen et al., 2021; Goldsack
et al., 2023b). However, the extent to which these
attributes are required within a lay summary de-
pends largely on the specific needs of the reader.
For example, a scientist from a related field will
require less background information to understand
an article’s contents than an entirely non-technical
∗Corresponding author
reader, but they might still require domain-specific
jargon to be simplified or explained. Despite its
obvious benefits, to our knowledge, no work has
yet explored how we can enable such fine-grained
control over comprehensibility-related aspects for
lay summary generation.
In this paper, we propose ATLAS (ATtribute-
controlled LAy Summarization), a novel scientific
summarisation approach that aims to control four
attributes targeting distinct properties contributing
to the overall “layness" of the generated summary,
thus allowing it to cater to the specific needs of dif-
ferent audiences. Although recent attempts at text
simplification and story generation have had suc-
cess influencing the style (Martin et al., 2020; Kong
et al., 2021; Sheang and Saggion, 2021) and content
(Kong et al., 2021; Tang et al., 2024) of generated
text using fine-grained controllable attributes, no
work to our knowledge has explored this for sci-
entific summarisation. Luo et al. (2022) recently
addressed the task of readability-controlled scien-
tific summarisation, however, this is only done at
a binary level, training a model to produce either
a technical or non-technical summary based on a
single control token.
Our approach innovates by enabling a greater de-
gree of controllability through the flexible handling
of multiple attributes, allowing it to produce more
diverse summaries and better address the specific
needs of different audiences. Our results show that
ATLAS outperforms state-of-the-art baselines in
both automatic and human evaluations across three
summary types with varying levels of technicality.
Additional analyses confirm that attribute control
positively influences performance, and suggest the
selected control attributes are able to effectively
capture the difference between technical and non-
technical summaries.
337
2
Methodology
As discussed in §1, ATLAS aims to control four
targeted attributes. We use BART-base as the base
model for ATLAS as it represents the state-of-the-
art benchmark in previous lay summarisation works
(Guo et al., 2021; Goldsack et al., 2022).
Formally, each document x = (x1, x2, ..., xn) of
length n, where xi is the i-th token, is prepended
with a control token sequence l such that x =
(l, x1, x2s, ..., xn). l consists of our four selected
control tokens, each of which targets distinct char-
acteristics of the output summary that contributes
to its overall comprehensibility. We describe each
aspect below:
Length (L)
The length of the output summary
in characters. A more lay audience may require a
longer summary to aid comprehension.
Readability (R)
How easy it is to read the text.
This is measured using the Flesh-Kincaid Grade
Level (FKGL) metric, which estimates the reading
grade level (US) required to understand the gener-
ated text based on the total number of sentences,
words, and syllables present within it.
Background information (BG)
The percentage
of sentences classified as containing primarily back-
ground information. Intuitively, a more lay audi-
ence will require greater levels of background in-
formation to contextualise an article.
Content word entropy (CWE)
The average en-
tropy of content words. We hypothesise that jargon
terms are likely to possess higher entropy values,
thus lower average CWE is likely to be a prop-
erty of more lay text. Since jargon terms are pre-
dominately nouns, we extract noun phrases as con-
tent words using CoreNLP library (Manning et al.,
2014).We then follow Xiao et al. (2020) to calcu-
late I(xi) entropy of a given token xi as the nega-
tive logarithm of its generation probability P(xi),
which is directly extracted from a pre-trained lan-
guage model.
I(xi) = −logP(xi)
(1)
During model training, true attribute values (as
calculated on reference summaries) are used, allow-
ing the model to learn to associate attribute values
with summary properties. For all attributes, values
are discretized into 10 fixed-width bins depending
on their respective range in the train split (from
minimum to maximum observed value), resulting
in 10 unique control tokens for each attribute which
are added to the vocabulary. For each attribute at
test time, we use the most common bin value ob-
served for reference summaries of the training set
as attribute values.
3
Experimental Setup
Data.
We experiment on the biomedical lay sum-
marisation datasets introduced in Goldsack et al.
(2022), eLife (4.8k articles) and PLOS (27.5k arti-
cles), for which target lay summaries have been
shown to contain different levels of “layness".
Specifically, eLife’s lay summaries have been char-
acterized as longer, more readable, and more ab-
stractive than those of PLOS, as well as being em-
pirically observed to be suitable for a more lay audi-
ence. We, therefore, combine both of these datasets,
allowing us to expose ATLAS to a greater variety
of attribute values during training.1 For each article
in the combined dataset, we train our ATLAS to
produce both the technical abstract and lay sum-
mary, using our control attributes to differentiate
between them.
Evaluation.
We employ several automatic met-
rics to evaluate the performance of ATLAS. In line
with common summarisation practice, we calcu-
late ROUGE-1,2, and L variants (Lin, 2004) and
BERTScore (Zhang et al., 2019). We also measure
Dale-Chall Readability Score, a metric that esti-
mates US grade level based on the frequency of
common words.
Baselines.
To enable fair comparison, we rerun
many of the baseline approaches used by Gold-
sack et al. (2022) (which have the abstract included
in the input) on the combined datasets. Specif-
ically, we rerun the Lead-3, Lead-K, and oracle
heuristic baselines; TextRank (Mihalcea and Ta-
rau, 2004), LexRank (Erkan and Radev, 2004),
and HipoRank (Dong et al., 2021) unsupervised
models; and BART and BARTScaffold supervised
models. Here, we use the transformer-based BART
base model (Lewis et al., 2020), which we fine-tune
on our own datasets. BARTScaffold is the recre-
ation of a model from Goldsack et al. (2022) which
is trained using a binary control token (<abs> or
<lay>) to produce either an abstract or lay summary
for an article. This model is equivalent to that pro-
1To combine the datasets, we merge the training and vali-
dation sets. We evaluate on the test sets separately.
338
Figure 1: Visualisation of the density distributions of controllable attribute values for each summary type in the
combined train split.
posed by Luo et al. (2022), the only previous work
on controllable lay summarisation.2
Finally, we include two baselines based on Chat-
GPT (3.5-turbo), so as to compare against an ac-
cessible and widely used method of controlling
text generation (i.e., prompt engineering). Our first
GPT baseline (GPT3.5-zs) uses the following zero-
shot prompts: (i) “Summarize the following article
for an expert audience that is familiar with the tech-
nical aspects of the content” to generate technical
abstracts; (ii) “Summarize the following article for
a non-expert audience that has some familiarity
with the technical aspects of the content” to gener-
ate PLOS lay summaries, and (iii) “Summarize the
following article for a non-expert audience that has
no familiarity with the technical aspects of the con-
tent” to generate eLife lay summaries. Our second
GPT baseline (GPT3.5-mdc) replicates the method
of Turbitt et al. (2023), the best-performing team
of the recent BioLaySumm shared task (Goldsack
et al., 2023a). Based on in-context learning, this
method dynamically selects the maximum num-
ber of input-output examples that fit in the context
window (separated by the simple prompt “Expla-
nation:”) to generate lay summaries based on only
the article abstract.
Implementation Details.
As mentioned in §2,
we employ BART-base as our base model. We
train our ATLAS for a maximum of 5 epochs on a
GeForce GTX-1080Ti GPU, retaining the check-
point with the best average ROUGE-1/2/L score on
the validation set. We set the batch size to 1 and
keep the α scale factor (§2) at the default value of
0.2 from Kong et al. (2021).
For calculating control attributes, we use SciB-
ERT (Beltagy et al., 2019) for entropy calculation,
and we employ a BERT-based sequential classi-
2The original code for Luo et al. (2022) is not yet avail-
able at the time of writing and their results are reported on a
different dataset and thus are not comparable.
Summary type
Precision
Recall
F1
Abstract
0.69
0.75
0.72
eLife-Lay
0.71
0.71
0.71
PLOS-Lay
0.73
0.66
0.71
Table 1: Classifier performance for 3-way classification
between summary types on the combined test set.
fier (Cohan et al., 2019) trained on the PubMed-
RTC dataset (Dernoncourt and Lee, 2017) for back-
ground sentence classification (as described in
Goldsack et al. (2022)). We compute the FKGL
readability score using the textstat package.
4
Experimental Results
Discriminatory ability of control attributes.
To
validate the ability of our controllable attributes to
distinguish between different summary types, we
plot the distribution of attribute values for each type
in Figure 1. The figure suggests that, in combina-
tion, the attributes are able to capture characteristic
differences between summary types, as instances
in which two summary types share a similar distri-
bution for one attribute can typically be separated
by other attributes.3
To further evidence this, we use the training set
to train a simple logistic regression classifier, using
only the attribute values of the reference summaries
as features, to discriminate between reference sum-
mary types. The test set results in Table 1 show that
all summary types are classified with an F1-score
above 0.7, attesting to the discriminatory power of
our control attributes.
Summarisation performance.
Table 2 presents
the performance of ATLAS and baseline models
using automatic metrics on the test sets of PLOS
3E.g., PLOS lay summaries and abstracts have similar
readability distributions but differ in their comprehensibility,
length, and entropy distributions. Similarly, PLOS and eLife
lay summaries have similar comprehensibility distributions
but differ in their readability and length.
339
Model
Abstract
Lay summary - PLOS
Lay summary - eLife
R-1
R-2
R-L
BS
DCRS
FKGL
R-1
R-2
R-L
BS
DCRS
FKGL
R-1
R-2
R-L
BS
DCRS
FKGL
Heuristic
Lead-3
23.86
5.66
21.48
81.17
12.66
14.82
27.41
6.87
24.61
83.36
12.66
15.08
19.41
4.06
18.02
81.65
12.65
13.30
Lead-K
35.69
9.07
32.70
82.86
11.69
14.49
38.28
9.45
34.8
83.72
11.88
14.95
37.27
7.53
35.18
82.05
10.58
11.89
Oracle
60.08
27.48
55.95
87.35
11.12
15.15
57.82
23.92
53.37
87.13
11.20
15.28
48.92
13.42
46.30
82.94
10.51
13.18
Unsup.
TextRank
40.26
11.53
36.02
83.83
11.78
20.08
37.55
8.50
33.28
83.43
11.87
20.27
33.88
5.79
31.55
81.16
11.30
18.98
LexRank
38.22
13.06
35.42
83.85
9.70
14.23
31.20
9.09
28.72
82.97
9.70
14.59
32.25
5.73
30.45
80.67
9.68
13.32
HipoRank
36.95
10.19
33.89
83.22
12.15
14.46
37.67
9.22
34.28
83.68
12.15
14.69
31.50
5.17
29.68
80.88
12.13
12.13
Supervised
BART
43.34
13.14
39.80
85.48
11.33
14.40
43.52
12.09
39.67
85.70
11.29
14.54
31.17
6.74
29.20
83.55
11.15
13.87
BARTScaffold
43.13
12.87
39.66
85.33
11.10
14.14
43.73
12.22
39.92
85.67
11.30
14.58
43.01
10.82
40.54
84.88
9.68
11.85
GPT3.5-zs
28.69
6.52
15.04
82.76
11.70
14.32
42.74
12.70
22.28
86.32
10.40
13.19
33.72
8.45
16.95
84.36
10.36
13.03
GPT3.5-mdc
-
-
-
-
-
-
44.41
14.16
41.12
86.55
10.36
13.32
37.97
9.39
35.57
84.22
10.78
13.70
ATLAS
45.87
14.08
42.32
85.54
10.96
14.21
44.44
12.33
40.60
85.70
11.22
14.58
46.80
12.57
44.14
85.20
8.95
10.87
ATLASOracle
46.11
14.07
42.51
85.69
10.99
14.13
44.97
12.49
41.02
85.82
11.21
14.48
46.61
12.29
43.95
85.11
9.18
11.39
Table 2: Summarization performance on the PLOS and eLife test sets (abstracts combined). R = ROUGE F1 (↑),
BS = BERTScore (↑), DCRS = Dale-Chall Readability Score (↓), FKGL = Flesh-Kincaid Grade Level (↓). For
supervised models, we highlight the best score obtained for each metric in bold and underline second best.
and eLife. We include the results for ATLAS under
two conditions: 1) one utilizing the average value
for each attribute observed in the training data for
each summary type (ATLAS); and 2) one using
true attribute values obtained from gold standard
summaries (ATLASOracle), where ATLASOracle is
intended to provide an upper bound of the obtain-
able performance using our control attributes.
For all metrics, it is evident from Table 2 that
ATLAS exceeds the performance of all baseline
approaches for both eLife lay summaries and ab-
stracts, demonstrating a strong ability to control
the technicality of generated text whilst producing
high-quality summaries. Interestingly, although the
GPT3.5-mdc baseline achieves a slightly stronger
all-round performance for PLOS lay summaries, it
fails to maintain this for the more “lay" summaries
of eLife where ATLAS achieves significantly better
performance, indicating that our control attributes
can effectively capture these differences.
In all cases, ATLAS also achieves scores that are
comparable to (and sometimes exceeding) that of
ATLASOracle, suggesting that the use of the most
frequently observed bin value for control attributes
is effective for producing the appropriate character-
istics for each summary type.
Ablation study.
To assess the contribution of
each attribute to model performance, we conduct
an ablation study, evaluating ATLASOracle under
different configurations.4 Table 3 reports the re-
sults of this study for abstracts and lay summaries
on the combined test sets of PLOS and eLife.
The table shows that the removal of control at-
tributes has a significant detrimental effect on per-
formance. Additionally, when only a single at-
tribute is included, the length-based control has
4We use ATLASOracle as the subject of this experiment
rather than ATLAS to get a true reflection of each attribute’s
influence, rather than an approximation.
Model
Lay summary
Abstract
R-1
R-2
R-L
DCRS
R-1
R-2
R-L
DCRS
BART
41.68
11.29
38.12
11.27
43.34
13.14
39.80
11.33
+R
43.34
12.03
39.75
10.91
43.49
13.23
39.95
11.12
+BG
42.52
11.71
39.01
11.01
43.74
13.65
40.35
10.98
+CWE
41.58
11.21
38.04
11.28
44.23
13.48
40.56
11.35
+L
44.22
12.21
40.55
10.81
44.83
13.75
41.31
11.03
+L+BG
44.66
12.36
40.96
10.99
45.67
13.78
42.02
11.17
+L+R
44.52
12.10
40.73
10.92
45.54
13.64
41.78
11.21
+L+CWE
44.72
12.41
41.04
10.88
45.87
13.99
42.32
10.10
+L+R+BG
44.82
12.41
41.10
10.97
45.94
14.07
42.32
11.10
+L+R+CWE
44.83
12.39
41.05
10.90
45.60
13.63
41.84
11.21
+L+BG+CWE
45.01
12.56
41.38
10.88
46.04
14.16
42.44
11.06
ATLASOracle
45.22
12.47
41.45
10.91
46.11
14.07
42.51
10.99
Table 3: Ablation study on the ROUGE-based perfor-
mance of ATLAS under different configurations using
true attribute values. “+" denotes aspect addition. L =
Length, R = Readability, CWE = Content Word Entropy,
BG = Background information.
the highest ROUGE scores, particularly for lay
summaries. This is to be expected, as lay sum-
maries are known to differ significantly in length
between PLOS (avg. 175.6 words) and eLife (avg.
347.6 words). When employing attributes in com-
bination, we can see that the addition of content
word entropy control and the subsequent addition
of background information control have the great-
est benefit to performance for ATLAS with 2 and 3
attributes, respectively. Interestingly, no attribute
emerges clearly as the least effective as, although
readability score control is the only one not in-
cluded in the 3 attribute model, its inclusion in the
single attribute model has clear benefits for lay sum-
mary performance. This provides further evidence
that, in combination, our control attributes are able
to capture the differences between summary types
and effectuate them during generation.
Human evaluation.
To provide a comprehensive
assessment of the summaries generated, we con-
ducted a human evaluation involving our proposed
model ATLAS and the strongest baseline model
340
Criteria
eLife
PLOS
BART
ATLAS
BART
ATLAS
Comprehensiveness
2.30
2.65
2.00
2.55
Layness
2.60
3.05
2.10
2.45
Factuality
2.20
2.85
2.05
2.40
Table 4: Human evaluation on eLife and PLOS. Mean
evaluator ratings (1-5) obtained by BART and ATLAS
outputs for each metric.
(BART) using two experts.5 Specifically, adopting
a similar setting to the original that of Goldsack
et al. (2022), we take a random sample of 10 ar-
ticles from the test split of each dataset. Along-
side each model-generated lay summary, judges
are presented with both the abstract and reference
lay summary of the given article. We choose not
to provide judges with the full article text in an
effort to minimise the complexity of the evalua-
tion and the cognitive burden placed upon them.
Using 1-5 Likert scale, the judges are asked to
rate the model output based on three criteria: (1)
Comprehensiveness: to what extent does the model
output contain the information that might be neces-
sary for a non-expert to understand the high-level
topic of the article and the significance of the re-
search; (2) Layness: to what extent is the content
of the model output comprehensible (or readable)
to a non-expert, in terms of both structure and lan-
guage; (3) Factuality: to what extent is the model
generated lay summary factually consistent with
the two other provided summaries (i.e. abstract and
reference lay summary).6
Table 4 presents the average ratings from our
manual evaluation. We calculate the Cohan Kappa
scores to measure inter-rater reliability, where we
obtain values of 0.50 and 0.57 for eLife and PLOS,
attesting to the reliability of our evaluation. The
overall results suggest that our proposed method
performs better than the BART baseline in terms of
all three criteria on both datasets, attesting to their
quality. In terms of layness, the higher layness
scores observed in the eLife dataset compared to
the PLOS dataset align with the previous analysis
for the two datasets from (Goldsack et al., 2022).
Moreover, compared to baseline, it is worth noting
that our model outputs are judged to produce much
more factually correct outputs on both datasets, sug-
gesting our method generates fewer hallucinations.
5Both judges have experience in scientific research and
hold at least a bachelor’s degree.
6For example, for the “Layness“ criteria, a score of 5 is
equal to “highly lay" and a score of 1, “highly technical".
Model
FKGL
CLI
DCRS
PLOS
ATLAStechnical
15.11
14.21
11.64
ATLASlay
13.22
13.97
11.22
eLife
ATLAStechnical
14.77
14.02
11.32
ATLASlay
10.89
11.45
9.17
Table 5: Readability metrics for two versions of ATLAS
with highly lay and technical attribute values.
Controllability analysis.
To assess the extent to
which our control attributes enable controllability
over the overall layness of the text, we conduct a
further analysis using two additional versions of
ATLAS with highly lay or technical values. Specif-
ically, we create ATLASlay and ATLAStechnical
by selecting the lowest and highest attribute bins,
respectively, for which there are at least 100 obser-
vations in the training data (for all attributes other
than length which is kept constant).
We examine how these extreme attributes mani-
fest themselves in generated summaries by calculat-
ing the average readability values obtained by the
generated summaries for both datasets. We present
the results of the analysis in Table 5, which show
a significant divergence in the readability values
obtained by each model on both datasets. Inter-
estingly, this divergence is substantially wider for
summaries generated on eLife, the dataset which is
identified by Goldsack et al. (2022) as containing
lay summaries that are more “lay” than those of
PLOS, suggesting that exposure to more extreme
values whilst training on this dataset may enable
even greater controllability at inference time.7
5
Conclusion
In this paper, we introduce ATLAS, a model for
controllable lay summarisation that employs con-
trollable attribute tokens to influence various prop-
erties of the generated summary, enabling it to cater
to users of different levels of expertise. Using com-
bined datasets for biomedical lay summarisation
we perform multiple experiments whereby we con-
firm the ability of our selected control attributes
to discriminate between summary types, demon-
strate their effectiveness for controllable lay sum-
marisation, and further investigate their ability to
effectuate desired differences during generation.
7Examples of summaries generated by these models are
included in the Appendices.
341
Limitations
Although our results demonstrate that our selected
control attributes are able to effectively capture the
characteristics between summary types, it is highly
likely that there are additional attributes that we
have not explored that could benefit performance
for controllable lay summarisation. We plan to
explore this in future work, in addition to experi-
menting with more complex methods for enabling
controllability.
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A
Appendix
ChatGPT Baseline Prompts
The prompts pro-
vided to ChatGPT for each summary type are given
in Table 6. To ensure a fair comparison, we control
the length of the GPT baselines using the genera-
tion arguments, (e.g., max_new_tokens). Note that
we differentiate between the lay summary types
(namely, PLOS and eLife) based on distinctions
made by Goldsack et al. (2022), who recognise
PLOS’ summaries as the less “lay" of the two, mak-
ing them better suited to an audience with some
technical knowledge.
343
Summary Type
Prompt
Abstract
Summarize the following article for an expert audience that is familiar
with the technical aspects of the content
PLOS lay summary
Summarize the following article for a non-expert audience that has some
familiarity with the technical aspects of the content
eLife lay summary
Summarize the following article for a non-expert audience that has no
familiarity with the technical aspects of the content
Table 6: Prompts used for the GPT3.5-zs baseline for each summary type.
eLife
ATLAStechnical
The effects of muscle fatigue on motor learning under fatigue are poorly understood. Here, we investigated the
effect of fatigue on learning under a sequential pinch force task. Irrespective of whether the observed fatigue effects
are domain-specific or present in another task that is cognitive demanding but requires minimal force control, we
found that participants had impaired skill learning in both the fatigued and unfatigued effector. We replicated the
findings of experiment 1 and found that disruption of rTMS to the motor cortex (Cantarero et al ., 2013a) alleviated
the adverse effects of fatigue. Cortical excitability was similar to that observed in the untrained effector, but not in
the unfatigued. Altogether, our findings suggest that motor fatigue has a domain-dependent lasting effect on skill
learning. Future studies should focus on understanding the role of motor cortex excitability in the acquisition of
motor skills under fatigue, as well as the potential role for maladaptive memory formation under fatigued conditions.
Cortical and motor cortices should be included in training and rehabilitation regimens geared to improve motor skill
acquisition.
ATLASlay
Muscle fatigue is a neuromuscular phenomenon that can impair performance over time. People who experience
fatigue tend to be less able to learn a new motor skill than people who experience no fatigue. However, it is not
clear how fatigue affects the ability of people to learn new motor skills . One way to study the effects of fatigue is
to study how people learn a motor skill under fatigue conditions. One of the main challenges in studying motor
learning under fatigue is the so-termed “performance-learning" distinction In this study, participants were asked
to practice a motor task over two days and then had to catch up to the skill performance level of the non-fatigued
group. Unexpectedly , participants who were only fatigued at the end of the training were less likely to learn the
motor skill. This suggests that fatigue has a domain-specific lasting effect on the learning of a skill. ernas et al. now
show that people who are unable to recover the motor task under fatigue are more likely to be unable to learn their
motor skill when they are not fatigued. The experiments show that when people are trained to perform the task,
their ability to recover from fatigue is severely impaired. This effect is due to a change in the strength of the motor
cortex, a region of the brain that is involved in learning and memory.
Figure 2: An case study from the eLife test set comparing summaries generated under highly lay and technical
attribute values (with the length attribute being kept constant).
344
PLOS
ATLAStechnical
In this paper, we explore the conditions under which associations between antigenic, metabolic and virulence
properties of strains within pneumococcal populations and predict how these may shift under vaccination. In this
work , we use a conceptual framework to investigate the dynamics of associations between serotype, serotype
and serotype-specific immunity in pneumococcus populations. We find that antigenic type (AT) is the principal
determinant of non-capsular virulence factors (VF) , whereas MT is the major determinant. AT and MT are highly
non-random; MT and AT are co-evolved and co-expressed. ET and CT are also found to be highly correlated,
suggesting that they have synergistically adapted to a particular metabolic niche. IT and LD are found to have
similar patterns of linkage disequilibrium (LD) than randomly selected genes not associated with metabolic/transport
processes; AT is associated with a higher frequency of LD LD than MT LD; CT LD=0.013). CT is the first
mathematical model to explain the non-overlapping association between serotypic and serotypes. TCT BC LD
is a useful tool for predicting the potential impact of vaccination on the prevalence of serotypes associated with
non-vaccine serotypes and for predicting how they may change under vaccination and vaccine serotype replacement.
ATLASlay
Pneumococcal populations are highly diverse in non-antigenic genes and are commonly classified into sequence
types (ST) by Multi Locus Sequence Typing (MLST) of seven metabolic housekeeping genes. STs have been
documented to occur regularly throughout the past 7 decades, yet many studies (eg) show an intriguing pattern of
largely non-overlapping associations between serotype and ST. It has been noted that many STs that were previously
associated with vaccine serotypes now occur in association with non-vaccine serotypes. It has been proposed that a
combination of immune-mediated interference between identical antigenic types and direct competition between
identical metabolic types can generate non-overlapping association between antigenic and STs in populations of the
bacterial pathogen Neisseria meningitidis . In this paper, we explore whether pneumococcal population structure,
can be explained within a similar conceptual framework. in which pathogen strains are profiled by antigenic type,
AT, metabolic type (MT) and additional non-capsular virulence factors (VF).
Figure 3: An case study from the eLife test set comparing summaries generated under highly lay and technical
attribute values (with the length attribute being kept constant).
345