Taking into account Inter-sentence Similarity for Update Summarization

Maâli Mnasri (1) , Gaël de Chalendar (2, 1) , Olivier Ferret (1)

1 DIASI - Département Intelligence Ambiante et Systèmes Interactifs
2 LVIC - Laboratoire Vision et Ingénierie des Contenus

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.

Domains: Computer Science [cs]; Information Retrieval [cs.IR]; Computer Science [cs]; Document and Text Processing

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⟩