{"title":"插入修辞谓语进行准抽象概括","authors":"Horacio Saggion","doi":"10.5555/1937055.1937090","DOIUrl":null,"url":null,"abstract":"We investigate the problem of inserting rhetorical predicates (e.g. \"to present\", \"to discuss\", \"to indicate\", \"to show\") during non extractive summary generation and compare various algorithms for the task which we trained over a set of human written summaries. The algorithms which use a set of features previously introduced in the summarization literature achieve between 57% to 62% accuracy depending on the machine learning algorithm used. We draw conclusions with respect to the use of context during predicate prediction.","PeriodicalId":120472,"journal":{"name":"RIAO Conference","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Inserting rhetorical predicates for quasi-abstractive summarization\",\"authors\":\"Horacio Saggion\",\"doi\":\"10.5555/1937055.1937090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We investigate the problem of inserting rhetorical predicates (e.g. \\\"to present\\\", \\\"to discuss\\\", \\\"to indicate\\\", \\\"to show\\\") during non extractive summary generation and compare various algorithms for the task which we trained over a set of human written summaries. The algorithms which use a set of features previously introduced in the summarization literature achieve between 57% to 62% accuracy depending on the machine learning algorithm used. We draw conclusions with respect to the use of context during predicate prediction.\",\"PeriodicalId\":120472,\"journal\":{\"name\":\"RIAO Conference\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"RIAO Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5555/1937055.1937090\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"RIAO Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5555/1937055.1937090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Inserting rhetorical predicates for quasi-abstractive summarization
We investigate the problem of inserting rhetorical predicates (e.g. "to present", "to discuss", "to indicate", "to show") during non extractive summary generation and compare various algorithms for the task which we trained over a set of human written summaries. The algorithms which use a set of features previously introduced in the summarization literature achieve between 57% to 62% accuracy depending on the machine learning algorithm used. We draw conclusions with respect to the use of context during predicate prediction.