Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs
This addresses the lack of systems for providing complete argumentative structure from arbitrary text, which is useful for general usage in argument mining.
The paper tackles the problem of automatically extracting argumentative structures from natural language text by presenting a pipeline that transforms German and English texts into graph-based argument representations, with results showing it can detect new connections between statements.
This paper targets the automated extraction of components of argumentative information and their relations from natural language text. Moreover, we address a current lack of systems to provide complete argumentative structure from arbitrary natural language text for general usage. We present an argument mining pipeline as a universally applicable approach for transforming German and English language texts to graph-based argument representations. We also introduce new methods for evaluating the results based on existing benchmark argument structures. Our results show that the generated argument graphs can be beneficial to detect new connections between different statements of an argumentative text. Our pipeline implementation is publicly available on GitHub.