NewsCubeSum: A Personalized Multidimensional News Update Summarization System

Dingding Wang, Lei Li, Tao Li
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Abstract

Popular online publishers produce huge amount of news articles every day, so it is important to summarize the most up-to-the-minute information to help users quickly know the progresses of their interested news events. In this paper, we develop NewsCubeSum, a novel personalized news summarization system utilizing OLAP and supervised sentence selection techniques to generate brief summaries delivering news updates in multiple dimensions (such as time, entity, and topic). An illustrative case study and experimental results on summarization performance comparisons are provided to show the effectiveness of NewsCubeSum.
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NewsCubeSum:个性化多维新闻更新汇总系统
热门的网络出版商每天都会产生大量的新闻文章,所以总结最新的信息,帮助用户快速了解他们感兴趣的新闻事件的进展是很重要的。在本文中,我们开发了NewsCubeSum,这是一个新颖的个性化新闻摘要系统,利用OLAP和监督句选择技术来生成简短的摘要,提供多维(如时间、实体和主题)的新闻更新。通过实例分析和总结性能比较的实验结果,证明了NewsCubeSum的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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