8. A storage medium storing instructions which when executed by a digital processor perform a method for characterizing classes of a probabilistic classifier or clustering system including (i) computing divergences of classes from other classes respective to candidate phrases based on probabilistic model parameters of the probabilistic classifier or clustering system, and (ii) characterizing classes using candidate phrases that provide substantial computed divergence from other classes effective for distinguishing the class from other classes, wherein the divergences computing (i) includes: computing a word-based divergence based on one or more probabilistic model parameters of the probabilistic classifier or clustering system that profile one or more words of the candidate phrase, computing a phrase-based divergence based on one or more probabilistic model parameters of the probabilistic classifier or clustering system that profile training documents containing the candidate phrase, and combining the word-based divergence and the phrase-based divergence to determine the divergence.
11. The storage medium as set forth in claim 8 , wherein the characterizing (ii) includes performing a multi-document summarization ranking of the candidate phrases based on the computed divergences.