Summarizing text documents
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TL;DR
An analysis of news-article summaries generated by sentence selection, using a normalized version of precision-recall curves with a baseline of random sentence selection to evaluate features and empirical results show the importance of corpus-dependent baseline summarization standards, compression ratios and carefully crafted long queries.
Abstract
Article Free Access Share on Summarizing text documents: sentence selection and evaluation metrics Authors: Jade Goldstein Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PAView Profile , Mark Kantrowitz Just Research, 4616 Henry Street, Pittsburgh, PA Just Research, 4616 Henry Street, Pittsburgh, PAView Profile , Vibhu Mittal Just Research, 4616 Henry Street, Pittsburgh, PA Just Research, 4616 Henry Street, Pittsburgh, PAView Profile , Jaime Carbonell Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrievalAugust 1999 Pages 121–128https://doi.org/10.1145/312624.312665Published:01 August 1999Publication History 264citation3,037DownloadsMetricsTotal Citations264Total Downloads3,037Last 12 Months222Last 6 weeks42 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF