  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.
