Machine Learning of Generic and User-Focused Summarization

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dc.creatorMani, Inderjeet
dc.creatorBloedorn, Eric
dc.date1998-11-02
dc.date.accessioned2026-07-07T03:23:47Z
dc.date.available2026-07-07T03:23:47Z
dc.descriptionA key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use of machine learning on a training corpus of documents and their abstracts to discover salience functions which describe what combination of features is optimal for a given summarization task. The method addresses both "generic" and user-focused summaries.
dc.descriptionIn Proceedings of the Fifteenth National Conference on AI (AAAI-98), p. 821-826
dc.identifierhttps://arxiv.org/abs/cs/9811006
dc.identifierhttp://arxiv.org/abs/cs/9811006
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/33079
dc.subjectComputation and Language
dc.subjectMachine Learning
dc.subjectI.2.6; I.2.7
dc.titleMachine Learning of Generic and User-Focused Summarization
dc.typetext

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