Utilizing microblogs for automatic news highlights extraction

Wei Zhongyuwei, Wei Gao

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

25 Citations (Scopus)

Abstract

Story highlights form a succinct single-document summary consisting of 3-4 highlight sentences that reflect the GIST of a news article. Automatically producing news highlights is very challenging. We propose a novel method to improve news highlights extraction by using microblogs. The hypothesis is that microblog posts, although noisy, are not only indicative of important pieces of information in the news story, but also inherently "short and sweet" resulting from the artificial compression effect due to the length limit. Given a news article, we formulate the problem as two rank-then-extract tasks: (1) we find a set of indicative tweets and use them to assist the ranking of news sentences for extraction; (2) we extract top ranked tweets as a substitute of sentence extraction. Results based on our news-tweets pairing corpus indicate that the method significantly outperform some strong baselines for single-document summarization.

Original languageEnglish
Title of host publicationCOLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers
PublisherAssociation for Computational Linguistics, ACL Anthology
Pages872-883
Number of pages12
ISBN (Print)9781941643266
Publication statusPublished - 2014
Event25th International Conference on Computational Linguistics, COLING 2014 - Dublin, Ireland
Duration: 23 Aug 201429 Aug 2014

Other

Other25th International Conference on Computational Linguistics, COLING 2014
CountryIreland
CityDublin
Period23/8/1429/8/14

    Fingerprint

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Zhongyuwei, W., & Gao, W. (2014). Utilizing microblogs for automatic news highlights extraction. In COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers (pp. 872-883). Association for Computational Linguistics, ACL Anthology.