  The automatic summarization of multimedia sources is an important task that
facilitates the understanding of an individual by condensing the source while
maintaining relevant information. In this paper we focus on audio summarization
based on audio features and the probability of distribution divergence. Our
method, based on an extractive summarization approach, aims to select the most
relevant segments until a time threshold is reached. It takes into account the
segment's length, position and informativeness value. Informativeness of each
segment is obtained by mapping a set of audio features issued from its
Mel-frequency Cepstral Coefficients and their corresponding Jensen-Shannon
divergence score. Results over a multi-evaluator scheme shows that our approach
provides understandable and informative summaries.
