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The ability to access embedded knowledge makes complex networks extremely promising for natural language processing, which normally requires deep knowledge representation that is not accessible with first-order statistics. In this paper, we demonstrate that features of complex networks, which have been shown to correlate with text quality, can be used to evaluate summaries. The metrics are the average degree, cluster coefficient, and the extent to which the dynamics of network growth deviates from a straight line. They were found to be much smaller for the high-quality, manual summaries, and increased for automatic summaries, thus pointing to a loss of quality, as expected. We also discuss the comparative performance of automatic summarizers.