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Ranking Sentences for Extractive Summarization with Reinforcement Learning

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Original languageEnglish
Title of host publicationThe 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Place of PublicationNew Orleans, Louisiana
PublisherAssociation for Computational Linguistics (ACL)
Pages1747-1759
Number of pages13
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

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.

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