Argument Mining Driven Analysis of Peer-Reviews


  • Michael Fromm LMU Munich
  • Evgeniy Faerman LMU Munich
  • Max Berrendorf LMU Munich
  • Siddharth Bhargava LMU Munich
  • Ruoxia Qi LMU Munich
  • Yao Zhang LMU Munich
  • Lukas Dennert LMU Munich
  • Sophia Selle LMU Munich
  • Yang Mao LMU Munich
  • Thomas Seidl LMU Munich



AI for Conference Organization and Delivery (AICOD), Applications, Text Classification & Sentiment Analysis


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 dataset of peer-reviews 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. Also, the process remains interpretable, since the extracted arguments can be highlighted in a review without detaching them from their context.




How to Cite

Fromm, M., Faerman, E., Berrendorf, M., Bhargava, S., Qi, R., Zhang, Y., Dennert, L., Selle, S., Mao, Y., & Seidl, T. (2021). Argument Mining Driven Analysis of Peer-Reviews. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 4758-4766.



AAAI Technical Track Focus Area on AI for Conference Organization and Delivery