Automatic recognition of topic transition for newspaper articles and application to document summary

自动识别报纸文章的主题转换并应用于文档摘要

基本信息

批准号：
15500086
负责人：
SUZUKI Yoshimi
金额：
$ 2.5万
依托单位：
University of Yamanashi
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

项目摘要

In this study, we paid attention to the automatic summary for the newspaper articles.The following was researched as a first step to summarize multi-documents precisely.(1)A subject template is made from the large-scale corpus, and we extract the subsequent articles of a target article using that subject template correctly. The extracted subsequent articles are classified in the subject cluster.(2)Every subject cluster is summarized, and the whole of the subsequent articles is summarized in consideration of a connection between the clusters.Regarding (1), we proposed a method for topic tracking using subject templates and machine learning (support vector machines). And also, we showed that our methods can extract subsequent articles with high accuracy using large corpus (the corpus by Topic detection and Tracking and articles of Mainichi Shimbun newspaper) (research paper 4,5).Regarding (2), we found that we have to extract synonyms of each word for multi-document summarization. We proposed a method to identify synonym pairs from Japanese newspaper (3,4). For identifying synonyms, we compared Lin's method with Hindle's method and we found Lin's method is better than Hindle's method for Japanese documents.We proposed a method which is based on Lin's method for Japanese documents. Moreover, we performed some experiments of sentence extraction using automatically extracted synonym pairs and title of newspaper article (research paper 1,2). The method is as the following, firstly from newspaper articles we extracted synonyms of words in titles of newspaper articles using the proposed method which is based on Lin's method, then we performed sentence extraction using the results. The results show that identifying synonyms is useful for sentence extraction.

在这项研究中，我们关注报纸文章的自动摘要。以下是精确总结多重件的第一步。（1）由大规模语料库制作主题模板，我们提取了使用该主题模板正确提取目标文章的后续文章。（2）总结了每个主题群集，然后总结了每个受试者群集，并且考虑到群集之间的联系，总结了每个主题群集，然后将整个文章汇总。重新标记（1），我们提出了一种使用受试者模板和机器学习（支持向量机）的主题跟踪的方法。而且，我们还表明，我们的方法可以使用大型语料库（按主题检测和跟踪以及Mainichi Shimbun报纸的语料库进行高精度提取随后的文章（研究论文4,5）。奖励（2），我们发现我们必须为多文章汇总提取每个单词的同义词。我们提出了一种识别日本报纸（3,4）的同义成对的方法。为了识别同义词，我们将林的方法与Hindle的方法进行了比较，我们发现Lin的方法比Hindle的日本文档方法更好。我们提出了一种基于Lin的日本文档方法的方法。此外，我们使用自动提取的同义词对和报纸文章的标题进行了一些句子提取的实验（研究论文1,2）。该方法如下所示，首先是从报纸文章中，我们使用基于Lin方法的提议方法提取了报纸文章中的单词同义词，然后我们使用结果进行了句子提取。结果表明，识别同义词对于句子提取很有用。