{"title":"Automatic Summarization for Chinese Text Based on Sub Topic Partition and Sentence Features","authors":"Xueming Li, Jiapei Zhang, Minling Xing","doi":"10.1109/IPTC.2011.40","DOIUrl":null,"url":null,"abstract":"With the explosion of electronic information on web, there is the increasing requirement to obtain the information needed accurately and efficiently. In this article, a method of automatic summarization based on sub topic partition and sentence features is proposed, in which the sentence weight is computed based on LexRank algorithm combining with the score of its own features in every sub topic, such as its length, position, cue words and structure. In addition, we reduce redundancy of candidate sentence collection. With evaluation on six different genres of data sets, our method could get more comprehensive and high-quality summarization with less redundancy than the original LexRank algorithm.","PeriodicalId":388589,"journal":{"name":"2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing","volume":"24 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPTC.2011.40","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
With the explosion of electronic information on web, there is the increasing requirement to obtain the information needed accurately and efficiently. In this article, a method of automatic summarization based on sub topic partition and sentence features is proposed, in which the sentence weight is computed based on LexRank algorithm combining with the score of its own features in every sub topic, such as its length, position, cue words and structure. In addition, we reduce redundancy of candidate sentence collection. With evaluation on six different genres of data sets, our method could get more comprehensive and high-quality summarization with less redundancy than the original LexRank algorithm.
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