Neural Summarization by Extracting Sentences and Words

Jianpeng Cheng, Maria Lapata

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

Abstract

Traditional approaches to extractive summarization rely heavily on human engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor. This architecture allows us to develop different classes of summarization models which can extract sentences or words. We train our models on large scale corpora containing hundreds of thousands of document-summary pairs. Experimental results on two summarization datasets demonstrate that our models obtain results comparable to the state of the art without any access to linguistic annotation.
Original languageEnglish
Title of host publicationProceedings of the 54th Annual Meeting of the Association for Computational Linguistics
Place of PublicationBerlin, Germany
PublisherAssociation for Computational Linguistics
Pages484-494
Number of pages11
ISBN (Electronic)978-1-945626-00-5
DOIs
Publication statusPublished - 12 Aug 2016
Event54th Annual Meeting of the Association for Computational Linguistics - Berlin, Germany
Duration: 7 Aug 201612 Aug 2016
https://mirror.aclweb.org/acl2016/

Conference

Conference54th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2016
CountryGermany
CityBerlin
Period7/08/1612/08/16
Internet address

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