  This paper describes a method for multi-document update summarization that
relies on a double maximization criterion. A Maximal Marginal Relevance like
criterion, modified and so called Smmr, is used to select sentences that are
close to the topic and at the same time, distant from sentences used in already
read documents. Summaries are then generated by assembling the high ranked
material and applying some ruled-based linguistic post-processing in order to
obtain length reduction and maintain coherency. Through a participation to the
Text Analysis Conference (TAC) 2008 evaluation campaign, we have shown that our
method achieves promising results.
