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基于词语对狄利克雷过程的时序摘要

席耀一 李弼程 李天彩 黄山奇

席耀一, 李弼程, 李天彩, 黄山奇. 基于词语对狄利克雷过程的时序摘要. 自动化学报, 2015, 41(8): 1452-1460. doi: 10.16383/j.aas.2015.c150001
引用本文: 席耀一, 李弼程, 李天彩, 黄山奇. 基于词语对狄利克雷过程的时序摘要. 自动化学报, 2015, 41(8): 1452-1460. doi: 10.16383/j.aas.2015.c150001
XI Yao-Yi, LI Bi-Cheng, LI Tian-Cai, HUANG Shan-Qi. Temporal Summarization Based on Biterm Dirichlet Process. ACTA AUTOMATICA SINICA, 2015, 41(8): 1452-1460. doi: 10.16383/j.aas.2015.c150001
Citation: XI Yao-Yi, LI Bi-Cheng, LI Tian-Cai, HUANG Shan-Qi. Temporal Summarization Based on Biterm Dirichlet Process. ACTA AUTOMATICA SINICA, 2015, 41(8): 1452-1460. doi: 10.16383/j.aas.2015.c150001

基于词语对狄利克雷过程的时序摘要

doi: 10.16383/j.aas.2015.c150001
基金项目: 

国家社会科学基金(14BXW028)资助

详细信息
    作者简介:

    李弼程 解放军信息工程大学信息系统工程学院教授.主要研究方向为文本分析与理解,语音处理与识别,图像/视频处理与识别,信息融合.E-mail:lbclm@gmail.com

Temporal Summarization Based on Biterm Dirichlet Process

Funds: 

Supported by National Social Science Foundation of China (14BXW028)

  • 摘要: 时序摘要是按照时间顺序生成摘要, 对话题的演化发展进行概括. 已有的相关研究忽视或者不能准确发现句子中隐含的子话题信息. 针对该问题, 本文建立了一种新的主题模型, 即词语对狄利克雷过程, 并提出了一种基于该模型的时序摘要生成方法. 首先通过模型推理得到句子的子话题分布; 然后利用该分布计算句子的相关度和新颖度; 最后按时间顺序抽取与话题相关且新颖度高的句子组成时序摘要. 实验结果表明, 本文方法较目前的代表性研究方法生成了更高质量的时序摘要.
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出版历程
  • 收稿日期:  2015-01-04
  • 修回日期:  2015-04-08
  • 刊出日期:  2015-08-20

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