Abstract
Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.- Anthology ID:
- N18-1158
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long 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:
- 1747–1759
- Language:
- URL:
- https://aclanthology.org/N18-1158
- DOI:
- 10.18653/v1/N18-1158
- Cite (ACL):
- Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018. Ranking Sentences for Extractive Summarization with Reinforcement Learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1747–1759, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Ranking Sentences for Extractive Summarization with Reinforcement Learning (Narayan et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/N18-1158.pdf
- Code
- shashiongithub/Refresh
- Data
- CNN/Daily Mail