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Using Query Expansion In Graph-based Approach For Query-focused Multi-document Summarization

机译:在基于图的方法中使用查询扩展进行以查询为中心的多文档摘要

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摘要

This paper presents a novel query expansion method, which is combined in the graph-based algorithm for query-focused multi-document summarization, so as to resolve the problem of information limit in the original query. Our approach makes use of both the sentence-to-sentence relations and the sentence-to-word relations to select the query biased informative words from the document set and use them as query expansions to improve the sentence ranking result. Compared to previous query expansion approaches, our approach can capture more relevant information with less noise. We performed experiments on the data of document understanding conference (DUC) 2005 and DUC 2006, and the evaluation results show that the proposed query expansion method can significantly improve the system performance and make our system comparable to the state-of-the-art systems.
机译:本文提出了一种新颖的查询扩展方法,该方法结合了基于图的算法,以查询为重点的多文档摘要,从而解决了原始查询中的信息限制问题。我们的方法利用句子与句子之间的关系以及句子与单词之间的关系,从文档集中选择查询偏向的信息性词,并将其用作查询扩展,以改善句子排名结果。与以前的查询扩展方法相比,我们的方法可以以更少的噪声捕获更多相关信息。我们对文档理解会议(DUC)2005和DUC 2006的数据进行了实验,评估结果表明,所提出的查询扩展方法可以显着提高系统性能,并使我们的系统可与最新系统媲美。 。

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