| Title | Fuzzy Clustering for Topic Analysis and Summarization of Document Collections |
| Publication Type | Conference Paper |
| Year of Publication | 2007 |
| Refereed Designation | Refereed |
| Authors | Witte, R. [5], and S. Bergler [6] |
| Editors | Kobti, Z. [7], and D. Wu [8] |
| Conference Name | Proceedings of the 20th Canadian Conference on Artificial Intelligence (Canadian A.I. 2007) |
| Tertiary Title | LNAI |
| Volume | 4509 |
| Pagination | 476–488 |
| Date Published | May 28–30 |
| Publisher | Springer |
| Conference Location | Montréal, Québec, Canada |
| ISBN Number | 978-3-540-72664-7 |
| 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%. |
| URL | http://www.springerlink.com/content/hn505mw73w70x5vt/fulltext.pdf [9] |
| DOI | 10.1007/978-3-540-72665-4_41 [10] |
| 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% |
| Attachment | Size |
|---|---|
| fuzzy_clustering_CAI2007.pdf [11] | 375.22 KB |
