Ranking Sentences for Extractive Summarization with Reinforcement Learning

Shashi Narayan, Shay B. Cohen, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Place of PublicationNew Orleans, Louisiana
PublisherAssociation for Computational Linguistics (ACL)
Pages1747-1759
Number of pages13
ISBN (Print)978-1-948087-27-8
DOIs
Publication statusPublished - 6 Jun 2018
Event16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Hyatt Regency New Orleans Hotel, New Orleans, United States
Duration: 1 Jun 20186 Jun 2018
http://naacl2018.org/

Conference

Conference16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Abbreviated titleNAACL HLT 2018
CountryUnited States
CityNew Orleans
Period1/06/186/06/18
Internet address

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