%0 
%0 Conference Proceedings
%A Cselle, Gabor; Albrecht, Keno & Wattenhofer, Roger
%D 2007
%T BuzzTrack: topic detection and tracking in email.
%E Chin, David N.; Zhou, Michelle X.; Lau, Tessa A. & Puerta, Angel R.
%B Intelligent User Interfaces
%C 
%I ACM
%V 
%6
%N 
%P 190-197
%& 
%Y 
%S 
%7
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%9
%? 
%! 
%Z 
%@ 1-59593-481-2
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%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 conf/iui/2007
%# 
%$ 
%F conf/iui/CselleAW07
%K detection email topic 
%X 
%Z 
%U http://dblp.uni-trier.de/db/conf/iui/iui2007.html#CselleAW07
%+
%^

%0 
%0 Conference Proceedings
%A Fiscus, Jonathan G.; Doddington, George; Garofolo, John S. & Martin, Alvin
%D 1998
%T NIST's 1998 Topic Detection and Tracking Evaluation (TDT2)
%E 
%B Proc. of the DARPA Broadcast News Workshop
%C Virginia, US
%I 
%V 
%6
%N 
%P 
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%7
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%@ 
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%)
%*
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%1 
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%3 inproceedings
%4 
%# 
%$ 
%F Fiscus98
%K detection evaluation tdt topic tracking 
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Makkonen, Juha & Ahonen-Myka, Helena
%D 2003
%T Utilizing Temporal Information in Topic Detection and Tracking.
%E Koch, Traugott & Sølvberg, Ingeborg
%B ECDL
%C 
%I Springer
%V 2769
%6
%N 
%P 393-404
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-540-40726-X
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%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 conf/ercimdl/2003
%# 
%$ 
%F conf/ercimdl/MakkonenA03
%K detection tdt temporal topic 
%X 
%Z 
%U http://dblp.uni-trier.de/db/conf/ercimdl/ecdl2003.html#MakkonenA03
%+
%^

%0 
%0 Conference Proceedings
%A Makkonen, Juha; Ahonen-Myka, Helena & Salmenkivi, Marko
%D 2003
%T Topic Detection and Tracking with Spatio-Temporal Evidence.
%E Sebastiani, Fabrizio
%B ECIR
%C 
%I Springer
%V 2633
%6
%N 
%P 251-265
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-540-01274-5
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%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 conf/ecir/2003
%# 
%$ 
%F conf/ecir/MakkonenAS03
%K detection tdt temporal topic 
%X 
%Z 
%U http://dblp.uni-trier.de/db/conf/ecir/ecir2003.html#MakkonenAS03
%+
%^

%0 
%0 Journal Article
%A Newman, E.; Domn, W.; Stokes, N.; Carthy, J. & Dunnion, J.
%D 2004
%T Comparing Redundancy Removal Techniques for Multi-Document Summarisation
%E 
%B Stairs 2004: Proceedings of the Second Starting AI Researchers' Symposium
%C 
%I IOS Press
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
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%@ 
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%)
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%1 
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%3 article
%4 
%# 
%$ 
%F newman2004crr
%K detection duplicate redundancy removal sentence summarization 
%X 
%Z 
%U 
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%^

%0 
%0 Journal Article
%A Pons-Porrata, Aurora; Berlanga-Llavori, Rafael & Ruiz-Shulcloper, Jos\'e,
%D 2007
%T Topic discovery based on text mining techniques
%E 
%B Inf. Process. Manage.
%C 
%I Pergamon Press, Inc.
%V 43
%6
%N 3
%P 752--768
%& 
%Y 
%S 
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%@ 0306-4573
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%1 
%2 
%3 article
%4 
%# 
%$ 
%F 1224718
%K detection mining text topic 
%X In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.
%Z 
%U http://portal.acm.org/citation.cfm?id=1224718
%+
%^