Better Rewards Yield Better Summaries: Learning to Summarise Without References
Table 2: Summary-level correlation of learned reward functions. All results are averaged over 5-fold cross validations. Unlike the metrics in Table 1, all rewards in this table do not require reference summaries.
['Model', 'Encoder', '[ITALIC] Reg. loss (Eq. ( 1 ))  [ITALIC] ρ', '[ITALIC] Reg. loss (Eq. ( 1 ))  [ITALIC] r', '[ITALIC] Reg. loss (Eq. ( 1 )) G-Pre', '[ITALIC] Reg. loss (Eq. ( 1 )) G-Rec', '[ITALIC] Pref. loss (Eq. ( 3 ))  [ITALIC] ρ', '[ITALIC] Pref. loss (Eq. ( 3 ))  [ITALIC] r', '[ITALIC] Pref. loss (Eq. ( 3 )) G-Pre', '[ITALIC] Pref. loss (Eq. ( 3 )) G-Rec']
Table 2 shows the quality of different reward learning models. As a baseline, we also consider the feature-rich reward learning method proposed by Peyrard and Gurevych (see §2). MLP with BERT as en(2018) coder has the best overall performance. Specifically, BERT+MLP+Pref significantly outperforms (p < 0.05) all the other models that do not use BERT+MLP,