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- W833089468 abstract "Event detection, an important task in organizational environmental scanning, is to identify the onset of new events from streams of news stories. Existing event detection techniques identify whether a news story contains an unseen event generally by comparing the similarity between features of a new news story and past news stories. However, for illustration and comparison purposes, a news story may contain sentences or paragraphs that are not highly relevant to defining its event. The inclusion of such sentences and paragraphs in the similarity comparison by a traditional event detection technique might significantly degrade its detection effectiveness. Therefore, in this study, we propose and develop a summary-based event detection (SED) technique that first filters less relative sentences or paragraphs from each news story before performing feature-based event detection. Using a traditional event detection technique (i.e., INCR) as a performance benchmark, our empirical evaluation results suggest that the proposed SED technique achieve comparable or even better detection effectiveness than its benchmark technique." @default.
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- W833089468 date "2004-01-01" @default.
- W833089468 modified "2023-09-24" @default.
- W833089468 title "Use of Text Summarization for Supporting Event Detection" @default.
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