Model Selection in Summary Evaluation

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7181

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DC Field	Value	Language
dc.creator	Perez-Breva, Luis	-
dc.creator	Yoshimi, Osamu	-
dc.date	2004-10-20T20:48:55Z	-
dc.date	2004-10-20T20:48:55Z	-
dc.date	2002-12-01	-
dc.date.accessioned	2013-10-09T02:48:28Z	-
dc.date.available	2013-10-09T02:48:28Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-2002-023	-
dc.identifier	CBCL-222	-
dc.identifier	http://hdl.handle.net/1721.1/7181	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	A difficulty in the design of automated text summarization algorithms is in the objective evaluation. Viewing summarization as a tradeoff between length and information content, we introduce a technique based on a hierarchy of classifiers to rank, through model selection, different summarization methods. This summary evaluation technique allows for broader comparison of summarization methods than the traditional techniques of summary evaluation. We present an empirical study of two simple, albeit widely used, summarization methods that shows the different usages of this automated task-based evaluation system and confirms the results obtained with human-based evaluation methods over smaller corpora.	-
dc.format	1739841 bytes	-
dc.format	1972183 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-2002-023	-
dc.relation	CBCL-222	-
dc.subject	AI	-
dc.title	Model Selection in Summary Evaluation	-
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