This paper describes a system facilitating information retrieval in a set of textual documents by tackling the automatic titling and subtitling issue. Automatic titling here consists in extracting relevant noun phrases from texts as candidate titles. An original approach combining statistical criteria and noun phrases positions in the text helps collecting relevant titles and subtitles. So, the user may benefit from an outline of all the subjects evoked in a mass of documents, and easily find the information he/she is looking for. An evaluation on real data shows that the solutions given by this automatic titling approach are relevant.