Abstract
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 on CNN/Daily Mail dataset as well as strong improvements in a test-only transfer setup on DUC-2002.- Anthology ID:
- N18-2102
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 646–653
- Language:
- URL:
- https://aclanthology.org/N18-2102
- DOI:
- 10.18653/v1/N18-2102
- Cite (ACL):
- Ramakanth Pasunuru and Mohit Bansal. 2018. Multi-Reward Reinforced Summarization with Saliency and Entailment. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 646–653, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Reward Reinforced Summarization with Saliency and Entailment (Pasunuru & Bansal, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-2102.pdf
- Data
- CNN/Daily Mail, MultiNLI, SQuAD