Multi document summarization based on cross-document relation using voting technique

Y. J. Kumar, N. Salim, Albaraa Abuobieda, A. Tawfik
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引用次数: 12

Abstract

News articles which are available through online search often provide readers with large collection of texts. Especially in the case of news story, different news sources reporting on the same event usually returns multiple articles in response to a reader's search. In this work, we first identify cross-document relations from un-annotated texts using Genetic-CBR approach. Following that, we develop a new sentence scoring model based on voting technique over the identified cross-document relations. Our experiments show that incorporating the proposed methods in the summarization process yields substantial improvement over the mainstream methods. The performances of all methods were evaluated using ROUGE - a standard evaluation metric used in text summarization.
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基于投票技术的跨文档关系多文档摘要
通过在线搜索获得的新闻文章通常为读者提供大量的文本集合。特别是在新闻报道的情况下,不同的新闻来源报道同一事件,通常会根据读者的搜索返回多篇文章。在这项工作中,我们首先使用Genetic-CBR方法从未注释的文本中识别跨文档关系。在此基础上,我们开发了一个基于投票技术的句子评分模型。我们的实验表明,在摘要过程中纳入所提出的方法比主流方法产生了实质性的改进。使用ROUGE(一种用于文本摘要的标准评价指标)对所有方法的性能进行评价。
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