Language-independent Informative Topic Segmentation

In this paper, we present an innovative topic segmentation system based on a new informative similarity measure that takes into account word co-occurrence in order to avoid the accessibility to existing linguistic resources such as electronic dictionaries or lexico-semantic databases such as thesauri or ontology. Topic Segmentation is the task of breaking documents into topically coherent multi-paragraph subparts. Topic Segmentation has extensively been used in Information Retrieval and Text Summarization. In particular, our architecture proposes a languageindependent Topic Segmentation system that solves three main problems evidenced by previous research: systems based uniquely on lexical repetition that show reliability problems, systems based on lexical cohesion using existing linguistic resources that are usually available only for dominating languages and as a consequence do not apply to less favored languages and finally systems that need previously existing harvesting training data.