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Semantic Software Lab
Concordia University
Montréal, Canada

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
Refereed DesignationRefereed
AuthorsWitte, R., and S. Bergler
EditorsKobti, Z., and D. Wu
Conference NameProceedings of the 20th Canadian Conference on Artificial Intelligence (Canadian A.I. 2007)
Tertiary TitleLNAI
Date PublishedMay 28–30
Conference LocationMontréal, Québec, Canada
ISBN Number978-3-540-72664-7

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.


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


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


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