{"title":"An automatic normalized cut topic segmentation approach","authors":"Yuanyuan Jin, Bao-jian Gao, Ziran Zhang","doi":"10.1109/YCICT.2010.5713130","DOIUrl":null,"url":null,"abstract":"This paper presents an automatic topic segmentation approach based on subwords normalized cut (Ncut) for Chinese broadcast news, since the classical Ncut has a limitation that the number of segments has to be set as a prior. We abstract a text into a weighted undirected graph, where the nodes correspond to sentences and the weights of edges describe inter-sentence lexical similarities at Chinese subwords level, thus the segmentation task is formalized as a graph-partitioning problem under the Ncut criterion. In order to break through the limitation, we proposed a text dotplotting inspired method, which can evaluate the segmentation results and select the optimal number of segments automatically. Lastly, we put the whole approach into a machine learning framework, learning the best arguments on train set. Our method achieved relative improvement of 3% over non-automatic subwords Ncut, also the previous best method.","PeriodicalId":179847,"journal":{"name":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YCICT.2010.5713130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents an automatic topic segmentation approach based on subwords normalized cut (Ncut) for Chinese broadcast news, since the classical Ncut has a limitation that the number of segments has to be set as a prior. We abstract a text into a weighted undirected graph, where the nodes correspond to sentences and the weights of edges describe inter-sentence lexical similarities at Chinese subwords level, thus the segmentation task is formalized as a graph-partitioning problem under the Ncut criterion. In order to break through the limitation, we proposed a text dotplotting inspired method, which can evaluate the segmentation results and select the optimal number of segments automatically. Lastly, we put the whole approach into a machine learning framework, learning the best arguments on train set. Our method achieved relative improvement of 3% over non-automatic subwords Ncut, also the previous best method.