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

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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.