The distributed Web-based multi-document summarization system is conceived to enrich semantically Geographic Databases (GDB) (Faïz, 1999; Scholl et al., 1996). In fact, in a traditional database, for instance, a city is described by its alphanumeric features: name, population count, and so forth; however, in a GDB, it is further described by spatial attributes which indicate its position (coordinates) in the space and its shape (point, line, polygon, etc.). Although the use of this myriad of information (alphanumeric and spatial data), the GDB suffers from the lack of an exhaustive set of information describing in a quasi-complete way the entities handled by it (Faïz, 2001). Hence, Geographic Information System (GIS) is not able to provide the end-user with information not fed into the GDB and that is not inherent to the application for which the GIS is designed (Bâazaoui, Faïz & Ben Ghezala, 2001, 2003; Faïz, Abbassi & Boursier, 1998). For instance, given a map displayed on the screen, it is not possible to get economic or historical information about cities for a given country whenever the GIS is concerned only with administrative boundaries. Having this idea in mind, our intention is to profit from the huge mass of information available online to enrich semantically a GDB. To fulfill this purpose and to manage the great amount of documents retrieved from the Web in a quick and convenient fashion, we adopted the Text Mining techniques (Tan 1999; Weiss, Apte & Damerau, 1999) and more precisely the summarization. Indeed, with the fast growth in the amount of textual information available online and the multitude of documents reporting almost the same thing, there is clearly a strong need for automatic summarization that copes with not only one document at one time but a set of topically similar ones.