Multi-document summarization using sentence clustering

Virendrakumar Gupta, Tanveer J. Siddiqui
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引用次数: 47

Abstract

This paper presents an approach to query focused multi document summarization by combining single document summary using sentence clustering. Both syntactic and semantic similarity between sentences is used for clustering. Single document summary is generated using document feature, sentence reference index feature, location feature and concept similarity feature. Sentences from single document summaries are clustered and top most sentences from each cluster are used for creating multi-document summary. We observed an average F-measure of 0.33774 on DUC 2002 multi-document dataset, which is comparable to three best performing systems reported on the same dataset.
使用句子聚类的多文档摘要
本文提出了一种基于句子聚类的单文档摘要结合查询的多文档摘要方法。句子之间的句法和语义相似性用于聚类。使用文档特征、句子参考索引特征、位置特征和概念相似特征生成单个文档摘要。来自单个文档摘要的句子被聚类,每个聚类中最上面的句子被用于创建多文档摘要。我们观察到DUC 2002多文档数据集的平均f值为0.3374,这与在同一数据集上报告的三个表现最好的系统相当。
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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