Improving Text Segmentation by Combining Endogenous and Exogenous Methods

Topic segmentation was addressed by a large amount of work from which it is not easy to draw conclusions, especially about the need for knowledge. In this article, we propose to combine in the same framework two methods for improving the results of a topic segmenter based on lexical reiteration. The first one is endogenous and exploits the distributional similarity of words in a document for discovering its topics. These topics are then used to facilitate the detection of topical similarity between discourse units. The second approach achieves the same goal by relying on external resources. Two resources are tested: a network of lexical co-occurrences built from a large corpus and a set of word senses induced from this network. An evaluation of the two approaches and their combination is performed in a reference framework and shows the interest of this combination both for French and English.