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Fuzzy Clustering for Topic Analysis and Summarization of Document Collections

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TitleFuzzy Clustering for Topic Analysis and Summarization of Document Collections
Publication TypeConference Paper
Year of Publication2007
AuthorsWitte, R., and S. Bergler
Refereed DesignationRefereed
EditorsKobti, Z., and D. Wu
Conference NameProceedings of the 20th Canadian Conference on Artificial Intelligence (Canadian A.I. 2007)
Tertiary TitleLNAI
Volume4509
Pagination476–488
Date PublishedMay 28–30
PublisherSpringer
Conference LocationMontréal, Québec, Canada
ISBN Number978-3-540-72664-7
Abstract

Large document collections, such as those delivered by Internet search engines, are difficult and time-consuming for users to read and analyse. The detection of common and distinctive topics within a document set, together with the generation of multi-document summaries, can greatly ease the burden of information management. We show how this can be achieved with a clustering algorithm based on fuzzy set theory, which (i) is easy to implement and integrate into a personal information system, (ii) generates a highly flexible data structure for topic analysis and summarization, and (iii) also delivers excellent performance.

Notes

Our paper received the best paper award at Canadian AI 2007, which had an acceptance rate of 17.7%.

URLhttp://www.springerlink.com/content/hn505mw73w70x5vt/fulltext.pdf
DOI10.1007/978-3-540-72665-4_41
Copyright

Copyright © 2007 Springer-Verlag. This is the author's version of the work. It is posted here by permission of Springer for your personal use. Not for redistribution.

Acceptance Rate

17.7%

AttachmentSize
fuzzy_clustering_CAI2007.pdf375.22 KB