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Using Proximity in Query Focused Multi-document Extractive Summarization

机译:在查询中使用邻近的聚焦多文件抽取总结

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摘要

The query focused multi-document summarization tasks usually tend to answer the queries in the summary. In this paper, we suggest introducing an effective feature which can represent the relation of key terms in the query. Here, we adopt the feature of term proximity commonly used in the field of information retrieval, which has improved the retrieval performance according to the relative position of terms. To resolve the problem of data sparseness and to represent the proximity in the semantic level, concept expansion is conducted based on WordNet. By leveraging the term importance, the proximity feature is further improved and weighted according to the inverse term frequency of terms. The experimental results show that our proposed feature can contribute to improving the summarization performance.
机译:查询聚焦的多文件摘要任务通常倾向于在摘要中回答查询。在本文中,我们建议介绍一个有效的功能,可以代表查询中的关键术语关系。这里,我们采用了在信息检索领域中常见的术语接近度的特征,其根据术语的相对位置提高了检索性能。解决数据稀疏问题并表示语义级别的邻近,基于Wordnet进行概念扩展。通过利用术语重要性,根据术语的逆术语频率进一步改善和加权接近特征。实验结果表明,我们提出的功能可以有助于提高摘要性能。

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