In this paper, we are interested in the topic segmentation of Arabic texts. For this aim, we evaluate two based lexical cohesion algorithms: MinCutSeg and BayesSeg by using the Pk and WindowDiff metrics. To assess how well each algorithm works, each was applied on three datasets with longer texts from two different domains: transcribed multi-party conversations and written texts. After adaptation to the Arabic language, the test results show significant differences in performance depending on the types of documents.