| 摘要: |
| 从网络信息的动态演化性出发,对同一话题不同时序阶段的文档集合进行识别和分析,在度量演化内容差异性的基础上实现动态性,给出了两种实现动态多文档文摘的模型,即基于矩阵子空间分析和基于文本相似度累加的动态多文档文摘模型.在此基础上,提出了高效的动态句子加权方法.TAC 2008 的Update Summarization 测试数据上的实验证明了所提出的动态多文档文摘模型的有效性. |
| 关键词: 多文档文摘 差异性分析 矩阵模型 相似度累加 动态演化 |
| DOI：10.3724/SP.J.1001.2012.03999 |
| 分类号: |
| 基金项目:国家自然科学基金(60736014, 60773069, 61073130); 国家高技术研究发展计划(863)(2006AA010108) |
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| Dynamic Multi-Document Summarization Model |
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LIU Mei-Ling1,2, ZHENG De-Quan1, ZHAO Tie-Jun1, YU Yang2
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1.Ministry of Education-Microsoft Key Laboratory of Speech Language (Harbin Institute of Technology), Harbin 150001, China;2.College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
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| Abstract: |
| This paper introduces two models to describe dynamic evolution of network information: identify and analysis the document collection on the same topic in different stages. In order to construct dynamic of evolution content differences, two dynamic multi-document summarization models are presented, which are matrix subspace analysis model, text similarity cumulative model. Based on these models, some efficient dynamic sentence weighting algorithms are implemented. Experiments on the test data of Update Summarization in TAC 2008 and comparative results between new models and TAC 2008 evaluation, shows the effectiveness of the models. |
| Key words: multi-document summarization otherness analysis matrix model similarity cumulative dynamic evolvement |
