Argument Mining Driven Analysis of Peer-Reviews

TL;DR

This paper introduces an Argument Mining approach to analyze peer reviews, providing a new dataset and demonstrating how argument extraction can assist in decision-making and improve interpretability in the review process.

Contribution

It presents a novel Argument Mining method tailored for peer reviews, along with a new annotated dataset and empirical evidence of its effectiveness in aiding publication decisions.

Findings

Arguments in peer reviews differ from other domains.

Argument extraction helps identify key review parts.

Method improves interpretability of review analysis.

Abstract

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.