Semantic Summarization of Web Documents

Documents' summarization techniques automatically extract relevant information from different sources with respect to a list of topics: they can be profitably used by a variety of applications and in particular for automatic indexing and categorization in order to facilitate the production and delivery of new multimedia contents. In this paper we propose a novel approach for summarizing documents retrieved from the Internet: we propose to capture the semantic nature of a document, expressed in natural language, in order to retrieve a number of RDF triplets and to clusterize these ones aggregating similar information. An overview of the system and some preliminary results are described.