Abstract
Text summarizers automatically construct summaries of a natural-language document. This paper examines the use of text summarization within data mining, identifying the potential summarizers have for uncovering interesting and unexpected information. It describes the current state of the art in commercial summarization and current approaches to the evaluation of summarizers. The paper then proposes a new model for text summarization and suggests a new form of evaluation. It argues that for summaries to be truly useful within data mining, they must include concepts abstracted from the text in addition to sentences extracted from the text. The paper uses two news articles to illustrate its points.
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