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Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. The TDT Pilot Study ran from September 1996 through October 1997. The primary participants were DARPA, Carnegie Mellon University, Dragon Systems, and the University of Massachusetts at Amherst. This report summarizes the findings of the pilot study. The TDT work continues in a new project involving larger training and test corpora, more active participants, and a more broadly defined notion of “topic” than was used in the pilot study. The following individuals participated in the research reported. James Allan, UMass Brian Archibald, CMU Doug Beeferman, CMU Adam Berger, CMU Ralf Brown, CMU Jaime Carbonell, CMU Ira Carp, Dragon Bruce Croft, UMass, George Doddington, DARPA Larry Gillick, Dragon Alex Hauptmann, CMU John Lafferty, CMU Victor Lavrenko, UMass Xin Liu, CMU Steve Lowe, Dragon Paul van Mulbregt, Dragon Ron Papka, UMass Thomas Pierce, CMU Jay Ponte, UMass Mike Scudder, UMass Charles Wayne, DARPA Jon Yamron, Dragon Yiming Yang, CMU
Allan et al. (Sat,) studied this question.