Discovering Topic Boundaries for Text Summarization Based on Word Co-occurrence

Topic Segmentation is the task of breaking documents into topically coherent multiparagraph subparts. In particular, Topic Segmentation is extensively used in Text Summarization to provide more coherent results by taking into account raw document structure. However, most methodologies are based on lexical repetition that show evident reliability problems or rely on harvesting linguistic resources that are usually available only for dominating languages and do not apply to less favored and emerging languages. In order to tackle these drawbacks, we present an innovative Topic Segmentation system based on a new informative similarity measure based on word co-occurrences and evaluate it on a set of web documents belonging to a single domain.