{"title":"以“简报”为重点总结非文本事件","authors":"Mohit Kumar, Dipanjan Das, Alexander I. Rudnicky","doi":"10.5555/1931390.1931411","DOIUrl":null,"url":null,"abstract":"We describe a learning-based system for generating reports based on a mix of text and event data. The system incorporates several stages of processing, including aggregation, template-filling and importance ranking. Aggregators and templates were based on a corpus of reports evaluated by human judges. Importance and granularity were learned from this corpus as well. We find that high-scoring reports (with a recall of 0.89) can be reliably produced using this procedure given a set of oracle features. The report drafting system is part of a learning cognitive assistant RADAR, and is used to describe its performance.","PeriodicalId":120472,"journal":{"name":"RIAO Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Summarizing non-textual events with 'Briefing' focus\",\"authors\":\"Mohit Kumar, Dipanjan Das, Alexander I. Rudnicky\",\"doi\":\"10.5555/1931390.1931411\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe a learning-based system for generating reports based on a mix of text and event data. The system incorporates several stages of processing, including aggregation, template-filling and importance ranking. Aggregators and templates were based on a corpus of reports evaluated by human judges. Importance and granularity were learned from this corpus as well. We find that high-scoring reports (with a recall of 0.89) can be reliably produced using this procedure given a set of oracle features. The report drafting system is part of a learning cognitive assistant RADAR, and is used to describe its performance.\",\"PeriodicalId\":120472,\"journal\":{\"name\":\"RIAO Conference\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"RIAO Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5555/1931390.1931411\",\"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/10.5555/1931390.1931411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Summarizing non-textual events with 'Briefing' focus
We describe a learning-based system for generating reports based on a mix of text and event data. The system incorporates several stages of processing, including aggregation, template-filling and importance ranking. Aggregators and templates were based on a corpus of reports evaluated by human judges. Importance and granularity were learned from this corpus as well. We find that high-scoring reports (with a recall of 0.89) can be reliably produced using this procedure given a set of oracle features. The report drafting system is part of a learning cognitive assistant RADAR, and is used to describe its performance.
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