Abstract
Seeking bits of useful information from a large amount of data on the Web still remains a difficult and time consuming task for a wide range of people such as students, reporters, and many other types of professionals. This problem requires to investigate new ways to handle and process information, that has to be delivered in a rather small space, retrieved in a short time, and represented as accurately as possible. This is surely one of the most important reasons for searching suitable and efficient summarization techniques capable of "distilling" the most important information from a variety of logically related sources, as the one returned from classic search engines, in order to produce a short, concise and grammatically meaningful version of information spread out in pages and pages of texts. In this paper we present a summarizer system, named iWIN (information on the Web In a Nutshell), that is able to perform an automatic summarization of multiple documents through: a semantic analysis of the text, a ranking method used to evaluate the relevance of the information for the specific user, a clustering method based on the document representation in terms of set of triplets (subject, verb, object) and a sentences' selection/ordering process to make the final summary as much readable as possible. Some preliminary results about system performances obtained using the ROUGE evaluation software are presented and discussed.