# Argument Mining Driven Analysis of Peer-Reviews

arXiv: https://arxiv.org/abs/2012.07743 · HTML: https://mlsanity.com/paper/2012.07743/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 16 of 16)

- [ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing](https://mlsanity.com/paper/2306.00622/) (2023)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions](https://mlsanity.com/paper/2110.08520/) (2021)
- [Automatic Analysis of Substantiation in Scientific Peer Reviews](https://mlsanity.com/paper/2311.11967/) (2023)
- [Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals](https://mlsanity.com/paper/2311.03998/) (2023)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision](https://mlsanity.com/paper/2406.00197/) (2024)
- [PeerArg: Argumentative Peer Review with LLMs](https://mlsanity.com/paper/2409.16813/) (2024)
- [Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments](https://mlsanity.com/paper/2505.22137/) (2025)
- [COMPARE: A Taxonomy and Dataset of Comparison Discussions in Peer Reviews](https://mlsanity.com/paper/2108.04366/) (2021)
- [ALL-IN-ONE: Multi-Task Learning BERT models for Evaluating Peer Assessments](https://mlsanity.com/paper/2110.03895/) (2021)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [Which Review Aspect Has a Greater Impact on the Duration of Open Peer Review in Multiple Rounds? -- Evidence from Nature Communications](https://mlsanity.com/paper/2606.21904/) (2026)
- [AI-Assisted Authoring for Transparent, Data-Driven Documents](https://mlsanity.com/paper/2601.06027/) (2025)
- [Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review](https://mlsanity.com/paper/2609.05947/) (2026)
- [Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architectures](https://mlsanity.com/paper/2609.39225/) (2026)

## Abstract (arXiv)

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.