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PSG: a two-layer graph model for document summarization

机译:PSG:用于文档摘要的两层图模型

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摘要

Graph model has been widely applied in document summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is presented, that not only sentences relevance but also phrases relevance information included in sentences are utilized. In a word, we construct a phrase-sentence two-layer graph structure model (PSG) to summarize document(s) . We use this model for generic document summarization and query-focused summarization. The experimental results show that our model greatly outperforms existing work.
机译:图模型以句子为图节点,句子之间的相似度为边缘,被广泛应用于文档摘要中。本文提出了一种新颖的文档摘要图模型,该模型不仅利用句子相关性,而且利用句子中包含的短语相关性信息。总之,我们构建了一个短语句子两层图结构模型(PSG)来概括文档。我们使用此模型进行通用文档摘要和针对查询的摘要。实验结果表明,我们的模型大大优于现有工作。

著录项

  • 来源
    《Frontiers of computer science in China》 |2014年第1期|119-130|共12页
  • 作者

    Heng CHEN; Hai JIN; Feng ZHAO;

  • 作者单位

    Service Computing Technology and System Lab & Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    Service Computing Technology and System Lab & Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    Service Computing Technology and System Lab & Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    relationship graph; Markov random walk; document summarization;

    机译:关系图马尔可夫随机行走;文件汇总;

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