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In this paper we introduce a machine learning approach for automatic text segmentation. Our text segmenter clusters text-segments containing similar concepts. It first discovers the different concepts present in a text, each concept being defined as a set of representative terms. After that the text is partitioned into coherent paragraphs using a clustering technique based on the Classification Maximum Likelihood approach. We evaluate the effectiveness of this technique on sets of concatenated paragraphs from two collections, the 7 sectors and the 20 Newsgroups corpus, and compare it to a baseline text segmentation technique proposed by Salton et al.

Unsupervised Learning with Term Clustering for Thematic Segmentation of Texts