{"title":"自动生成EM的标题","authors":"Paul E. Kennedy, Alexander Hauptmann","doi":"10.1145/336597.336670","DOIUrl":null,"url":null,"abstract":"Our prototype automatic title generation system inspired by statistical machine-translation approaches [1] treats the document title like a translation of the document. Titles can be generated without extracting words from the document. A large corpus of documents with human-assigned titles is required for training title \"translation\" models. On an f1 evaluation score our approach outperformed another approach based on Bayesian probability estimates [7].","PeriodicalId":42447,"journal":{"name":"Digital Library Perspectives","volume":"117 1","pages":"230-231"},"PeriodicalIF":1.1000,"publicationDate":"2000-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Automatic title generation for EM\",\"authors\":\"Paul E. Kennedy, Alexander Hauptmann\",\"doi\":\"10.1145/336597.336670\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Our prototype automatic title generation system inspired by statistical machine-translation approaches [1] treats the document title like a translation of the document. Titles can be generated without extracting words from the document. A large corpus of documents with human-assigned titles is required for training title \\\"translation\\\" models. On an f1 evaluation score our approach outperformed another approach based on Bayesian probability estimates [7].\",\"PeriodicalId\":42447,\"journal\":{\"name\":\"Digital Library Perspectives\",\"volume\":\"117 1\",\"pages\":\"230-231\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2000-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Library Perspectives\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/336597.336670\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Library Perspectives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/336597.336670","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
Our prototype automatic title generation system inspired by statistical machine-translation approaches [1] treats the document title like a translation of the document. Titles can be generated without extracting words from the document. A large corpus of documents with human-assigned titles is required for training title "translation" models. On an f1 evaluation score our approach outperformed another approach based on Bayesian probability estimates [7].
期刊介绍：
Digital Library Perspectives (DLP) is a peer-reviewed journal concerned with digital content collections. It publishes research related to the curation and web-based delivery of digital objects collected for the advancement of scholarship, teaching and learning. And which advance the digital information environment as it relates to global knowledge, communication and world memory. The journal aims to keep readers informed about current trends, initiatives, and developments. Including those in digital libraries and digital repositories, along with their standards and technologies. The editor invites contributions on the following, as well as other related topics: Digitization, Data as information, Archives and manuscripts, Digital preservation and digital archiving, Digital cultural memory initiatives, Usability studies, K-12 and higher education uses of digital collections.
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