  Due to the large amount of textual information available on Internet, it is
of paramount relevance to use techniques that find relevant and concise
content. A typical task devoted to the identification of informative sentences
in documents is the so called extractive document summarization task. In this
paper, we use complex network concepts to devise an extractive Multi Document
Summarization (MDS) method, which extracts the most central sentences from
several textual sources. In the proposed model, texts are represented as
networks, where nodes represent sentences and the edges are established based
on the number of shared words. Differently from previous works, the
identification of relevant terms is guided by the characterization of nodes via
dynamical measurements of complex networks, including symmetry, accessibility
and absorption time. The evaluation of the proposed system revealed that
excellent results were obtained with particular dynamical measurements,
including those based on the exploration of networks via random walks.
