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Monolingual text-to-text generation is an emerging research area in natural language processing. One reason for the interest in such generation systems is the possibility to automatically learn text-to-text generation strategies from aligned monolingual corpora. In this context, paraphrase detection can be seen as the task of aligning sentences that convey the same information but yet are written in different forms, thereby building a training set of rewriting examples. In this paper, we propose a new metric for unsupervised detection of paraphrases and test it over a set of standard paraphrase corpora. The results are promising as they outperform state-of-the-art measures developed for similar tasks.