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
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1747–1759
Language:
URL:
https://www.aclweb.org/anthology/N18-1158
DOI:
10.18653/v1/N18-1158
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1158.pdf