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First IEEE International Conference on Data Mining (ICDM'01)
An Experimental Comparison of Supervised and Unsupervised Approaches to Text Summarization
San Jose, California
November 29-December 02
ISBN: 0-7695-1119-8
The paper presents a direct comparison of supervised and unsupervised approaches to text summarization. As a representative supervised method, we use the C4.5 decision tree algorithm, extended with the Minimum Description Length Principle (MDL), and compare it against several unsupervised methods. It is found that a particular un-supervised method based on an extension of the K-means clustering algorithm, performs equal to and in some cases superior to the decision tree based method.
Citation:
Tadashi Nomoto, Yuji Matsumoto, "An Experimental Comparison of Supervised and Unsupervised Approaches to Text Summarization," icdm, pp.630, First IEEE International Conference on Data Mining (ICDM'01), 2001
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