Use of Text Summarization for Supporting Event Detection
Chih‐Ping Wei, Pao-Feng Wu, Yen‐Hsien Lee · Journal of the Association for Information Systems · 2004
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.