| Summary: | 碩士 === 國立臺北科技大學 === 電機工程系碩士班 === 90 === News web sites conveniently allow readers to peruse news online. Owing to rapid updating, the amount of news articles is so massive that readers have to spend much time on searching for desired news. Accumulating those related information and summarizing news events will shorten readers’ access time.
This thesis proposes a novel automatic summarization system to retrieve on-line news articles efficiently. This proposed system can collect news, calculate the weights of keywords, cluster news events, and then summarize them automatically. This system pays more attention to clustering similar news events according to the news characters and generates a summary for each cluster. In addition, this thesis proposes two summary generating approaches: the similarity value density approach and keyword value density approach.
For experimentation, the summary generated from the proposed system is compared with the one generated by majority selection of readers. Besides, the evaluations for these two proposed summary generating approaches have also been prosecuted to verify the suitability of the proposed system.
The proposed system can be adopted to collect, categorize, retrieve and cluster news events. In addition to reducing searching and retrieval time on the web sites, the proposed system can enable readers to quickly access news events via a news summary and, in doing so, effectively manage news retrieval system.
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