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Taking into account Inter-sentence Similarity for Update Summarization

Maâli Mnasri 1 Gaël de Chalendar 2, 1 Olivier Ferret 1 
2 LVIC - Laboratoire Vision et Ingénierie des Contenus
DIASI - Département Intelligence Ambiante et Systèmes Interactifs : DRT/LIST/DIASI
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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Contributor : Olivier Ferret Connect in order to contact the contributor
Submitted on : Friday, August 17, 2018 - 3:31:05 PM
Last modification on : Thursday, February 17, 2022 - 10:08:05 AM


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  • HAL Id : cea-01857884, version 1


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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