{"title":"使用文本表示质心(TRC)生成和评估文本摘要","authors":"Yanakorn Ruamsuk, A. Mingkhwan, H. Unger","doi":"10.1109/RI2C56397.2022.9910272","DOIUrl":null,"url":null,"abstract":"Short abstracts and text summarisation are gaining increasing the importance of tools for filtering out the most relevant articles due to the increased documents and information. The following article uses TRCs to support text summaries’ generation and evaluation. A new method of document summarisation has been introduced based on text-representing centroids (TRC). TRC-based similarity measure delivers good similarity estimations for both methods, which are in the excellent range of 75 percent on average.","PeriodicalId":403083,"journal":{"name":"2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Generating and Evaluating Text Summarisations using Text-representing Centroids(TRC)\",\"authors\":\"Yanakorn Ruamsuk, A. Mingkhwan, H. Unger\",\"doi\":\"10.1109/RI2C56397.2022.9910272\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Short abstracts and text summarisation are gaining increasing the importance of tools for filtering out the most relevant articles due to the increased documents and information. The following article uses TRCs to support text summaries’ generation and evaluation. A new method of document summarisation has been introduced based on text-representing centroids (TRC). TRC-based similarity measure delivers good similarity estimations for both methods, which are in the excellent range of 75 percent on average.\",\"PeriodicalId\":403083,\"journal\":{\"name\":\"2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C)\",\"volume\":\"142 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RI2C56397.2022.9910272\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RI2C56397.2022.9910272","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generating and Evaluating Text Summarisations using Text-representing Centroids(TRC)
Short abstracts and text summarisation are gaining increasing the importance of tools for filtering out the most relevant articles due to the increased documents and information. The following article uses TRCs to support text summaries’ generation and evaluation. A new method of document summarisation has been introduced based on text-representing centroids (TRC). TRC-based similarity measure delivers good similarity estimations for both methods, which are in the excellent range of 75 percent on average.