A risk minimization framework for extractive speech summarization

Shih Hsiang Lin, Berlin Chen

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

1 Citation (Scopus)

Abstract

In this paper, we formulate extractive summarization as a risk minimization problem and propose a unified probabilistic framework that naturally combines supervised and unsupervised summarization models to inherit their individual merits as well as to overcome their inherent limitations. In addition, the introduction of various loss functions also provides the summarization framework with a flexible but systematic way to render the redundancy and coherence relationships among sentences and between sentences and the whole document, respectively. Experiments on speech summarization show that the methods deduced from our framework are very competitive with existing summarization approaches.

Original languageEnglish
Title of host publicationACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Conference Proceedings
EditorsJan Hajic, Sandra Carberry, Stephen Clark
PublisherAssociation for Computational Linguistics (ACL)
Pages79-87
Number of pages9
ISBN (Electronic)1932432663, 9781932432664
Publication statusPublished - 2010
Event48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Uppsala, Sweden
Duration: 2010 Jul 112010 Jul 16

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
Volume2010-July
ISSN (Print)0736-587X

Other

Other48th Annual Meeting of the Association for Computational Linguistics, ACL 2010
Country/TerritorySweden
CityUppsala
Period2010/07/112010/07/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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