Document Modeling with External Attention for Sentence Extraction

Shashi Narayan, Ronald Cardenas, Nikolaos Papasarantopoulos, Shay Cohen, Mirella Lapata, Jiangsheng Yu, Yi Chang

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

Abstract

Document modeling is essential to a variety of natural language understanding tasks. We propose to use external information to improve document modeling for problems that can be framed as sentence extraction. We develop a framework composed of a hierarchical document encoder and an attention-based extractor with attention over external information. We evaluate our model on extractive document summarization (where the external information is image captions and the title of the document) and answer selection (where the external information is a question). We show that our model consistently outperforms strong baselines, in terms of both informativeness and fluency (for CNN document summarization) and achieves state-of-the-art results for answer selection on WikiQA and NewsQA.11Our TensorFlow code and datasets are publicly available at https://github.com/shashiongithub/Document-Models-with-Ext Information. 
Original languageEnglish
Title of host publication56th Annual Meeting of the Association for Computational Linguistics
Place of PublicationMelbourne, Australia
PublisherAssociation for Computational Linguistics
Pages2020-2030
Number of pages11
Publication statusPublished - Jul 2018
Event56th Annual Meeting of the Association for Computational Linguistics - Melbourne Convention and Exhibition Centre, Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018
http://acl2018.org/

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2018
Country/TerritoryAustralia
CityMelbourne
Period15/07/1820/07/18
Internet address

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