The dominant approach to argument mining has been to treat it as a machine learning problem based upon superficial text features, and to treat the relationships between arguments as either support or attack. However, accurately summarizing argumentation in scientific research articles requires a deeper understanding of the text and a richer model of relationships between arguments. First, this paper presents an argumentation scheme-based approach to mining a class of biomedical research articles. Argumentation schemes implemented as logic programs are formulated in terms of semantic predicates that could be obtained from a text by use of biomedical/biological natural language processing tools. The logic programs can be used to extract the underlying scheme name, premises, and implicit or explicit conclusion of an argument. Then this paper explores how arguments in a research article occur within a narrative of scientific discovery, how they are related to each other, and some implications.
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