Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization
Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization
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DOI：
10.1145/1390334.1390384
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发表时间：
2008-07
期刊：
影响因子：
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通讯作者：
Furu Wei;Wenjie Li;Q. Lu;Yanxiang He
中科院分区：
文献类型：
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作者：
Furu Wei;Wenjie Li;Q. Lu;Yanxiang He
句子排序是文档摘要中最受关注的问题。早期的研究人员提出了句子和术语之间的相互强化原则(MR)，用于通用单文档摘要中同时提取关键短语和显著句子。在这项工作中，我们将MR扩展到文档、句子和术语三个不同文本粒度的互增强链。其目的是为MRC提供一个通用的加固框架和一个正式的数学模型。更进一步，我们将查询影响引入到MRC中，以应对面向查询的多文档摘要的需要。虽然以前的摘要方法通常计算相似度而不考虑查询，但我们提出了一个查询敏感的相似度来衡量文本对之间的亲和度。在DUC 2005数据集上的实验结果表明，提出的查询敏感的MRC(Qs-MRC)是一种很有前途的摘要方法。
Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.