ARedSum: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Table 1: Full-length Rouge F1 evaluation (%) on the CNN/DailyMail test set. RG-1, RG-2, RG-L are short for Rouge-1, Rouge-2 and Rouge-L.
['Model', 'RG-1', 'RG-2', 'RG-L']
CNN/DailyMail. Results are comparable as we use the same non-anonymized version of CNN/DailyMail. We also find that the sequence generation models, i.e., NeuSum and our ARedSum-Seq, do not have clear advantage over other models regardless of their encoder network structure (i.e., Bert or other neural architectures). Our ARedSum-Seq perform similarly to BertSumExt. We attribute this to the fact that the target sequence in NeuSum and ARedSum-Seq is order-sensitive, while the final evaluation of summarization is not. As shown in the figure, BertSumExt has the best P@1,P@2 and P@3 among all, which is reasonable since ^S∗ is the target which it is optimized to learn. It indicates that TriBlk could filter out some sentences that are informative but redundant during selection, which harms precision but improves Rouge. In contrast, P@2 and P@3 of ARedSum-Ctx is between BertSumExt with and without TriBlk. Through learning towards Rouge gain given the previously extracted sentences, ARedSum-Ctx achieves the best Rouge scores with less harm to precision, which means that ARedSum-Ctx can better balance salience and redundancy.