Papers › Multi-Reward Reinforced Summarization with Saliency and Entailment
Multi-Reward Reinforced Summarization with Saliency and Entailment
Ramakanth Pasunuru, Mohit Bansal
Abstractive text summarization is the task of compressing and rewriting a long document into a short summary while maintaining saliency, directed logical entailment, and non-redundancy. In this work, we address these three important aspects of a good summary via a reinforcement learning approach with two novel reward functions: ROUGESal and Entail, on top of a coverage-based baseline. The ROUGESal reward modifies the ROUGE metric by up-weighting the salient phrases/words detected via a keyphrase classifier. The Entail reward gives high (length-normalized) scores to logically-entailed summaries using an entailment classifier. Further, we show superior performance improvement when these rewards are combined with traditional metric (ROUGE) based rewards, via our novel and effective multi-reward approach of optimizing multiple rewards simultaneously in alternate mini-batches. Our method achieves the new state-of-the-art results (including human evaluation) on the CNN/Daily Mail dataset as well as strong improvements in a test-only transfer setup on DUC-2002.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Abstractive Text Summarization | CNN / Daily Mail | ROUGESal+Ent RL | ROUGE-1 | 40.43 | #41 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | ROUGESal+Ent RL | ROUGE-2 | 18.00 | #41 of 53 | Archive leaderboard | report |
| Abstractive Text Summarization | CNN / Daily Mail | ROUGESal+Ent RL | ROUGE-L | 37.10 | #41 of 53 | Archive leaderboard | report |
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