@conference {WiBe_CAI2007,
	title = {Fuzzy Clustering for Topic Analysis and Summarization of Document Collections},
	booktitle = {Proceedings of the 20th Canadian Conference on Artificial Intelligence (Canadian A.I. 2007)},
	series = {LNAI},
	volume = {4509},
	year = {2007},
	note = {Our paper received the best paper award at Canadian AI 2007, which had an acceptance rate of 17.7\%.},
	month = {May 28{\textendash}30},
	pages = {476{\textendash}488},
	publisher = {Springer},
	organization = {Springer},
	address = {Montr{\'e}al, Qu{\'e}bec, Canada},
	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.},
	isbn = {978-3-540-72664-7},
	doi = {10.1007/978-3-540-72665-4_41},
	url = {http://www.springerlink.com/content/hn505mw73w70x5vt/fulltext.pdf},
	attachments = {https://www.semanticsoftware.info/system/files/fuzzy_clustering_CAI2007.pdf},
	author = {Ren{\'e} Witte and Sabine Bergler},
	editor = {Z. Kobti and D. Wu}
}
