Rafael Ferreira, R. Lins, L. Cabral, F. Freitas, S. Simske, M. Riss
{"title":"使用摘要策略的自动文档分类","authors":"Rafael Ferreira, R. Lins, L. Cabral, F. Freitas, S. Simske, M. Riss","doi":"10.1145/2682571.2797077","DOIUrl":null,"url":null,"abstract":"An efficient way to automatically classify documents may be provided by automatic text summarization, the task of creating a shorter text from one or several documents. This paper presents an assessment of the 15 most widely used methods for automatic text summarization from the text classification perspective. A naive Bayes classifier was used showing that some of the methods tested are better suited for such a task.","PeriodicalId":106339,"journal":{"name":"Proceedings of the 2015 ACM Symposium on Document Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automatic Document Classification using Summarization Strategies\",\"authors\":\"Rafael Ferreira, R. Lins, L. Cabral, F. Freitas, S. Simske, M. Riss\",\"doi\":\"10.1145/2682571.2797077\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An efficient way to automatically classify documents may be provided by automatic text summarization, the task of creating a shorter text from one or several documents. This paper presents an assessment of the 15 most widely used methods for automatic text summarization from the text classification perspective. A naive Bayes classifier was used showing that some of the methods tested are better suited for such a task.\",\"PeriodicalId\":106339,\"journal\":{\"name\":\"Proceedings of the 2015 ACM Symposium on Document Engineering\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2015 ACM Symposium on Document Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2682571.2797077\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 ACM Symposium on Document Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2682571.2797077","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Document Classification using Summarization Strategies
An efficient way to automatically classify documents may be provided by automatic text summarization, the task of creating a shorter text from one or several documents. This paper presents an assessment of the 15 most widely used methods for automatic text summarization from the text classification perspective. A naive Bayes classifier was used showing that some of the methods tested are better suited for such a task.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
