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This paper describes a system that uses speech recognition and clustered text news stories to automatically find story boundaries in an audio news broadcast and provides a semantic representation that can match audio news stories of similar content. This system creates a personal, synthetic newscast by extracting stories, based on user interests, from multiple hourly newscasts and then reassembling them into a single recording at the end of the day. Interaction is via graphical and telephone-based interfaces, with newscasts delivered over a local area network or to wireless audio pagers.
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