| Using LSA and text segmentation to improve automatic Chinese dialogue text summarization |
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| 作者姓名： | LIU Chuan-han WANG Yong-cheng ZHENG Fei LIU De-rong |
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| 作者单位： | [1]Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, China [2]Center for Biomimetic Sensing and Control Research, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China |
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| 基金项目： | Project （No. 2002AA119050） supported by the National Hi-Tech Research and Development Program （863） of China |
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| 摘 要： | Automatic Chinese text summarization for dialogue style is a relatively new research area. In this paper, Latent Semantic Analysis （LSA） is first used to extract semantic knowledge from a given document, all question paragraphs are identified, an automatic text segmentation approach analogous to Text＇filing is exploited to improve the precision of correlating question paragraphs and answer paragraphs, and finally some ＂important＂ sentences are extracted from the generic content and the question-answer pairs to generate a complete summary. Experimental results showed that our approach is highly efficient and improves significantly the coherence of the summary while not compromising informativeness.
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| 关 键 词： | 汉语对话 自动文本摘要 隐含语义分析 文本分析 对话方式 |
| 收稿时间： | 2006-07-04 |
| 修稿时间： | 2006-10-07 |
Using LSA and text segmentation to improve automatic Chinese dialogue text summarization |
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| LIU Chuan-han WANG Yong-cheng ZHENG Fei LIU De-rong.Using LSA and text segmentation to improve automatic Chinese dialogue text summarization[J].Journal of Zhejiang University Science,2007,8(1):79-87. |
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| Authors: | Chuan-han Liu Yong-cheng Wang Fei Zheng De-rong Liu |
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| Institution: | (1) Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China;(2) Center for Biomimetic Sensing and Control Research, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China |
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| Abstract: | Automatic Chinese text summarization for dialogue style is a relatively new research area. In this paper, Latent Semantic
Analysis (LSA) is first used to extract semantic knowledge from a given document, all question paragraphs are identified,
an automatic text segmentation approach analogous to TextTiling is exploited to improve the precision of correlating question
paragraphs and answer paragraphs, and finally some “important” sentences are extracted from the generic content and the question-answer
pairs to generate a complete summary. Experimental results showed that our approach is highly efficient and improves significantly
the coherence of the summary while not compromising informativeness.
Project (No. 2002AA119050) supported by the National Hi-Tech Research and Development Program (863) of China |
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| Keywords: | Automatic text summarization Latent semantic analysis (LSA) Text segmentation Dialogue style Coherence Question-answer pairs |
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