  In this paper we propose a graph-community detection approach to identify
cross-document relationships at the topic segment level. Given a set of related
documents, we automatically find these relationships by clustering segments
with similar content (topics). In this context, we study how different
weighting mechanisms influence the discovery of word communities that relate to
the different topics found in the documents. Finally, we test different mapping
functions to assign topic segments to word communities, determining which topic
segments are considered equivalent.
  By performing this task it is possible to enable efficient multi-document
browsing, since when a user finds relevant content in one document we can
provide access to similar topics in other documents. We deploy our approach in
two different scenarios. One is an educational scenario where equivalence
relationships between learning materials need to be found. The other consists
of a series of dialogs in a social context where students discuss commonplace
topics. Results show that our proposed approach better discovered equivalence
relationships in learning material documents and obtained close results in the
social speech domain, where the best performing approach was a clustering
technique.
