Despite the central role that argumentation plays in human communication, the computational linguistics community has paid relatively little attention in proposing a methodology for automatically identifying arguments and their relations in texts. Argumentation is intimately related with discourse structure, since an argument often spans more than one phrase, forming thus an entity with its own coherent internal structure. Moreover, arguments are linked between them either with a support, an attack or a rebuttal relation. Those argumentation relations are often realized via a discourse relation. Unfortunately, most of the discourse representation theories use trees in order to represent discourse, a format which is incapable of representing phenomena such as long distance attachments and crossed dependencies which are crucial for argumentation. A notable exception is Segmented Discourse Representation Theory (SDRT) (Asher and Lascarides, 2003). In this paper we show how SDRT can help identify arguments and their relations. We use counter-argumentation as our case study following Apotheloz (1989)and Amgoud and Prade (2012) showing how the identification of the discourse structure can greatly benefit the identification of the argumentation structure.