Intra-content term weighting for topic segmentation

Term weighting is an important task in many applications, such as information retrieval, extraction of significant words or automatic summarization. It translates the capacity of a term to discriminate a document within a collection, or a part of a document within a whole document. This paper deals with term weighting strategies in the context of lexical cohesion based topic segmentation. The aim is to propose a term weighting method which does not require any external information data. Weights are estimated from the content itself which is considered as a collection of mono-thematic documents. Two approaches are proposed and significant improvements are observed on a rich corpus covering various formats of Broadcast News shows from 8 French TV channels.