Ranking Sentences for Extractive Summarization with Reinforcement Learning

Shashi Narayan, Shay B. Cohen, Mirella Lapata


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/N18-1158.pdf
Code
 shashiongithub/Refresh
Data
CNN/Daily Mail