A fuzzy-rough hybrid approach to multi-document extractive summarization

Hsun Hui Huang, Horng Chang Yang, Yau Hwang Kuo

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

3 Citations (Scopus)

Abstract

To generate a multi-document extractive summary, the measurement of sentence relevance is of vital importance. Earlier work, exploring statistics of textual terms at the word (surface) level, faces the problem that the textual terms may be synonymous or ploysemous. This may lead to misrank sentence relevance and may cause redundant information presented in the generated summary. Furthermore, the relationships between concepts expressed by natural languages are inherently fuzzy, which invites the use of fuzzy set and rough set theory. In this paper, we investigate some sentence features from a concept-level space and apply a fuzzy-rough hybrid scheme to define a sentence relevance measure. Our approach is applied to the DUC 2006 multi-document summarization tasks. The experimental results show our approach is promising and demonstrate the effectiveness of fuzzy set and rough set theory in the application of text summarization.

Original languageEnglish
Title of host publicationProceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
Pages168-173
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 27
Event2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009 - Shenyang, China
Duration: 2009 Aug 122009 Aug 14

Publication series

NameProceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
Volume1

Other

Other2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
Country/TerritoryChina
CityShenyang
Period09-08-1209-08-14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Software

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