Abstractive news summarization based on event semantic link network

Wei Li, Lei He, Hai Zhuge

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

Abstract

This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC2006 and DUC2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.
Original languageEnglish
Title of host publicationThe 26th International Conference on Computational Linguistics
PublisherAssociation for Computational Linguistics
ISBN (Print)978-4-87974-702-0
Publication statusPublished - 11 Dec 2016
Event26th International Conference on Computational Linguistics: COLIN 2016 - Osaka, Japan
Duration: 11 Dec 201616 Dec 2016

Conference

Conference26th International Conference on Computational Linguistics
CountryJapan
CityOsaka
Period11/12/1616/12/16

Bibliographical note

This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http://
creativecommons.org/licenses/by/4.0/

Keywords

  • Summarization
  • sematnic link network
  • event

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  • Cite this

    Li, W., He, L., & Zhuge, H. (2016). Abstractive news summarization based on event semantic link network. In The 26th International Conference on Computational Linguistics Association for Computational Linguistics.