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This work presents and compare two approaches for the semantic segmentation of broadcast news: the first is based on social network analysis, the second is based on Poisson stochastic processes. The experiments are performed over 27 hours of material: preliminary results are obtained by addressing the problem of splitting different episodes of the same program into two parts corresponding to a news bulletin and a talk-show respectively. The results show that the transition point between the two parts can be detected with an average error of around three minutes, i.e. roughly 5 percent of each episode duration.