Update summarization is a new challenge in multi-document summarization focusing on summarizing a set of recent documents relatively to another set of earlier documents. We present an unsupervised probabilistic approach to model novelty in a document collection and apply it to the generation of update summaries. The new model, called D UAL S UM, results in the second or third position in terms of the ROUGE metrics when tuned for previous TAC competitions and tested on TAC-2011, being statistically indistinguishable from the winning system. A manual evaluation of the generated summaries shows state-of-the art results for D UAL S.
