EM Clustering Algorithm for Automatic Text Summarization

Automatic text summarization has emerged as a technique for accessing only to useful information. In order to known the quality of the automatic summaries produced by a system, in DUC 2002 (Document Understanding Conference) has developed a standard human summaries called gold collection of 567 documents of single news. In this conference only five systems could outperforms the baseline heuristic in single extractive summarization task. So far, some approaches have got good results combining different strategies with language-dependent knowledge. In this paper, we present a competitive method based on an EM clustering algorithm for improving the quality of the automatic summaries using practically non language-dependent knowledge. Also, a comparison of this method with three text models is presented.