A bottom-up approach to sentence ordering for multi-document summarization

Danushka Bollegala*, Naoaki Okazaki, Mitsuru Ishizuka

*Corresponding author for this work

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

23 Citations (Scopus)

Abstract

Ordering information is a difficult but important task for applications generating natural-language text. We present a bottom-up approach to arranging sentences extracted for multi-document summarization. To capture the association and order of two textual segments (eg, sentences), we define four criteria, chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion until we obtain the overall segment with all sentences arranged. Our experimental results show a significant improvement over existing sentence ordering strategies.

Original languageEnglish
Title of host publicationCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Pages385-392
Number of pages8
Volume1
Publication statusPublished - 2006
Externally publishedYes
Event21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, NSW
Duration: 2006 Jul 172006 Jul 21

Other

Other21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
CitySydney, NSW
Period06/7/1706/7/21

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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