Automatic generation of arguments is an important task that can be useful for many applications. For instance, the ability to generate coherent arguments during a debate can be useful when determining strengths of supporting evidence. However, with limited technologies that automatically generate arguments, the development of computational models for debates, as well as other areas, is becoming increasingly important. For this task, we focused on a promising argumentation model: the Toulmin model. The Toulmin model is both well-structured and general, and has been shown to be useful for policy debates. In this preliminary work we attempted to generate, with a given topic motion keyword or phrase, Toulmin model arguments by developing a computational model for detecting arguments spanned across multiple documents. This paper discusses our subjective results, observations, and future work.