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

Improving Update Summarization by Revisiting the MMR Criterion