{"id":260016,"date":"2000-07-31T21:07:11","date_gmt":"2000-08-01T04:07:11","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=260016"},"modified":"2021-01-05T21:46:59","modified_gmt":"2021-01-06T05:46:59","slug":"the-automated-acquisition-of-topic-signatures-for-text-summarization","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-automated-acquisition-of-topic-signatures-for-text-summarization\/","title":{"rendered":"The Automated Acquisition of Topic Signatures for Text Summarization"},"content":{"rendered":"<p>In order to produce a good summary, one has to identify the most relevant portions of a given text. We describe in this paper a method for automatically training topic signatures{sets of related words, with associated weights, organized around head topics{and illustrate with signatures we created with 6,194 TREC collection texts over 4 selected topics. We describe the possible integration of topic signatures with ontologies and its evaluaton on an automated text summarization system.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In order to produce a good summary, one has to identify the most relevant portions of a given text. We describe in this paper a method for automatically training topic signatures{sets of related words, with associated weights, organized around head topics{and illustrate with signatures we created with 6,194 TREC collection texts over 4 selected topics. 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