  Computational Argumentation in general and Argument Mining in particular are
important research fields. In previous works, many of the challenges to
automatically extract and to some degree reason over natural language arguments
were addressed. The tools to extract argument units are increasingly available
and further open problems can be addressed. In this work, we are presenting the
task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of
Aspect Term Extraction (ATE) and Nested Segmentation (NS). At the first
instance, we create and release an annotated corpus with aspect information on
the token-level. We consider aspects as the main point(s) argument units are
addressing. This information is important for further downstream tasks such as
argument ranking, argument summarization and generation, as well as the search
for counter-arguments on the aspect-level. We present several experiments using
state-of-the-art supervised architectures and demonstrate their performance for
both of the subtasks. The annotated benchmark is available at
https://github.com/trtm/ABAM.
