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 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||