Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining

The dominant approach to argumentation mining has been to treat argumentation scheme detection as a machine learning problem based upon superficial text features, and to treat the relationships between arguments as support or attack. However, applications such as accurately representing and summarizing argumentation in scientific research articles require a deeper understanding of the text and a richer model of relationships between arguments. This paper presents a semantic rule-based approach to extracting individual arguments, and demonstrates the need for a richer model of inter-argument relationships in biomedical/biological research articles.