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Vol.25 No.4
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A Study on an Effective Event Detection Method for Event-Focused News Summarization

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2008, v.25 no.4, pp.227-243
https://doi.org/10.3743/KOSIM.2008.25.4.227


& (2008). A Study on an Effective Event Detection Method for Event-Focused News Summarization. Journal of the Korean Society for Information Management, 25(4), 227-243, https://doi.org/10.3743/KOSIM.2008.25.4.227
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Abstract

This study investigates an event detection method with the aim of generating an event-focused news summary from a set of news articles on a certain event using a multi-document summarization technique. The event detection method first classifies news articles into the event related topic categories by employing a SVM classifier and then creates event clusters containing news articles on an event by a modified single pass clustering algorithm. The clustering algorithm applies a time penalty function as well as cluster partitioning to enhance the clustering performance. It was found that the event detection method proposed in this study showed a satisfactory performance in terms of both the F-measure and the detection cost.

keywords
event detection, event-focused news summarization, support vector machine classifier, single pass algorithm, time penalty function, 사건탐지, 사건중심 뉴스기사 자동요약, 지지벡터기, 싱글패스 알고리즘, 시간벌점함수, event detection, event-focused news summarization, support vector machine classifier, single pass algorithm, time penalty function

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