  Peer reviewing is a central process in modern research and essential for
ensuring high quality and reliability of published work. At the same time, it
is a time-consuming process and increasing interest in emerging fields often
results in a high review workload, especially for senior researchers in this
area. How to cope with this problem is an open question and it is vividly
discussed across all major conferences. In this work, we propose an Argument
Mining based approach for the assistance of editors, meta-reviewers, and
reviewers. We demonstrate that the decision process in the field of scientific
publications is driven by arguments and automatic argument identification is
helpful in various use-cases. One of our findings is that arguments used in the
peer-review process differ from arguments in other domains making the transfer
of pre-trained models difficult. Therefore, we provide the community with a new
peer-review dataset from different computer science conferences with annotated
arguments. In our extensive empirical evaluation, we show that Argument Mining
can be used to efficiently extract the most relevant parts from reviews, which
are paramount for the publication decision. The process remains interpretable
since the extracted arguments can be highlighted in a review without detaching
them from their context.
