ARedSum: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Table 2: Full-length Rouge F1 (%) evaluation on the NYT50 test set.
['Model', 'RG-1', 'RG-2', 'RG-L']
NYT50. In contrast to CNN/DailyMail, we observe that TriBlk has harmed the performance of BertSumExt on NYT50. This indicates that TriBlk filters out more sentences that have high Rouge gain on NYT50 than CNN/DailyMail, causing more drop of Rouge. It also shows that sentences in oracle summaries have more trigram overlap on NYT50 than CNN/DailyMail, which implies that redundancy removal on NYT50 may have limited gains and a simple unified rule (TriBlk) applying on all the documents could harm the performance. The results of NeuSum and HiBert are taken from their original papers while we obtained the rest of the results by re-running the models.