Identifying Multidocument Relations

The digital world generates an incredible accumulation of information. This results in redundant, complementary, and contradictory information, which may be produced by several sources. Applications as multidocument summarization and question answering are committed to handling this information and require the identification of relations among the various texts in order to accomplish their tasks. In this paper we first describe an effort to create and annotate a corpus of news texts with multidocument relations from the Crossdocument Structure Theory (CST) and then present a machine learning experiment for the automatic identification of some of these relations. We show that our results for both tasks are satisfactory.