| 基于LDA主题特征的自动文摘方法 |
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| 引用本文： | 张明慧,王红玲,周国栋. 基于LDA主题特征的自动文摘方法[J]. 计算机应用与软件, 2011, 0(10) |
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| 作者姓名： | 张明慧 王红玲 周国栋 |
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| 作者单位： | 苏州大学计算机科学与技术学院;江苏省计算机信息处理技术重点实验室; |
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| 基金项目： | 国家自然科学基金(60673041,60873150); 江苏省高校自然科学重大基础研究项目(08KJA520002) |
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| 摘 要： | 近年来概率主题模型受到了研究者的广泛关注,LDA(Latent Dirichlet Allocation)模型是主题模型中具有代表性的概率生成模型之一,它能够检测文本的隐含主题。提出一个基于LDA模型的主题特征,该特征计算文档的主题分布与句子主题分布的距离。结合传统多文档自动文摘中的常用特征,计算句子权重,最终根据句子的分值抽取句子形成摘要。实验结果证明,加入LDA模型的主题特征后,自动文摘的性能得到了显著的提高。
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| 关 键 词： | 自动文摘 LDA 主题模型 多文档 |
AN AUTOMATIC SUMMARIZATION APPROACH BASED ON LDA TOPIC FEATURE |
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| Zhang Minghui Wang Hongling Zhou Guodong. AN AUTOMATIC SUMMARIZATION APPROACH BASED ON LDA TOPIC FEATURE[J]. Computer Applications and Software, 2011, 0(10) |
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| Authors: | Zhang Minghui Wang Hongling Zhou Guodong |
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| Affiliation: | Zhang Minghui Wang Hongling Zhou Guodong(School of Computer Science and Technology,Soochow University,Suzhou 215006,Jiangsu,China)(Jiangsu Provincial Key Laboratory of Computer Information Processing Technology,China) |
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| Abstract: | Probabilistic topic models have received considerable attentions in recent years.LDA model,as a topic model,is one representative among probabilistic generative models,which is used to detect latent topics from documents.In this paper,an LDA-model-based topic feature is proposed.The feature is applied to calculating the distance between distributed document topics and distributed sentence topics.By combining common features in conventional multi-document automatic summarizations,sentences are ranked,and the... |
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| Keywords: | Automatic document summarization Latent dirichlet allocation(LDA) Topic model Multi-document |
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