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Conference Papers Year : 2017

Taking into account Inter-sentence Similarity for Update Summarization

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Abstract

Following Gillick and Favre (2009), a lot of work about extractive summarization has modeled this task by associating two contrary constraints: one aims at maximizing the coverage of the summary with respect to its information content while the other represents its size limit. In this context, the notion of redundancy is only implicitly taken into account. In this article, we extend the framework defined by Gillick and Favre (2009) by examining how and to what extent integrating semantic sentence similarity into an update summarization system can improve its results. We show more precisely the impact of this strategy through evaluations performed on DUC 2007 and TAC 2008 and 2009 datasets.
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Dates and versions

cea-01857884 , version 1 (17-08-2018)

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  • HAL Id : cea-01857884 , version 1

Cite

Maâli Mnasri, Gaël de Chalendar, Olivier Ferret. Taking into account Inter-sentence Similarity for Update Summarization. Eighth International Joint Conference on Natural Language Processing (IJCNLP 2017), short paper session, Nov 2017, Taipei, Taiwan. pp.204-209. ⟨cea-01857884⟩
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