Datasets:
File size: 13,048 Bytes
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---
dataset_info:
- config_name: actionability
features:
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dtype: string
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struct:
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- name: grounding_specificity
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- name: verifiability
struct:
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- name: helpfulness
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- config_name: professional_tone
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struct:
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- config_name: valid_point
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configs:
- config_name: actionability
data_files:
- split: gold
path: actionability/gold-*
- split: silver
path: actionability/silver-*
- split: hard
path: actionability/hard-*
- split: full
path: actionability/full-*
- config_name: addressed_to_author
data_files:
- split: gold
path: addressed_to_author/gold-*
- split: silver
path: addressed_to_author/silver-*
- split: full
path: addressed_to_author/full-*
- config_name: combined_main_aspects
data_files:
- split: full
path: combined_main_aspects/full-*
- config_name: context_experiment_with_paper_text
data_files:
- split: full
path: context_experiment_with_paper_text/full-*
- config_name: grounding_specificity
data_files:
- split: gold
path: grounding_specificity/gold-*
- split: silver
path: grounding_specificity/silver-*
- split: hard
path: grounding_specificity/hard-*
- split: full
path: grounding_specificity/full-*
- config_name: helpfulness
data_files:
- split: gold
path: helpfulness/gold-*
- split: silver
path: helpfulness/silver-*
- split: hard
path: helpfulness/hard-*
- split: full
path: helpfulness/full-*
- config_name: professional_tone
data_files:
- split: gold
path: professional_tone/gold-*
- split: silver
path: professional_tone/silver-*
- split: full
path: professional_tone/full-*
- config_name: valid_point
data_files:
- split: gold
path: valid_point/gold-*
- split: silver
path: valid_point/silver-*
- split: full
path: valid_point/full-*
- config_name: verifiability
data_files:
- split: gold
path: verifiability/gold-*
- split: silver
path: verifiability/silver-*
- split: hard
path: verifiability/hard-*
- split: full
path: verifiability/full-*
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
size_categories:
- 1K<n<10K
---
# RevUtil: Measuring the Utility of Peer Reviews for Authors
[📄 Paper](https://aclanthology.org/2025.emnlp-main.1476/)
[💻 GitHub Repository](https://github.com/bodasadallah/RevUtil)
---
## 📚 Overview
Providing **constructive feedback** to authors is a key goal of peer review. To support research on evaluating and generating useful peer review comments, we introduce **RevUtil**, a dataset for measuring the utility of peer review feedback.
RevUtil focuses on four main aspects of review comments:
- **Actionability** – Can the author act on the comment?
- **Grounding & Specificity** – Is the comment concrete and tied to the paper?
- **Verifiability** – Can the statement be checked against the paper?
- **Helpfulness** – Does the comment assist the author in improving their work?
---
## 🧑🔬 RevUtil Human
- **1,430** review comments from real peer reviews.
- Each comment is annotated independently by **three human raters**.
- Labels are provided as `"gold"` (3/3 agreement), `"silver"` (2/3), or `"none"` (no agreement).
**Key columns:**
| Column | Description |
| ------------------- | --------------------------------------------------------------------------- |
| `paper_id` | ID of the reviewed paper |
| `venue` | Conference or journal name |
| `focused_review` | Full review (weakness + suggestion sections) |
| `review_point` | Individual review comment being evaluated |
| `id` | Unique ID for the review point |
| `batch` | Annotation batch/study identifier |
| `ASPECT` | Dictionary with annotators and their labels |
| `ASPECT_label` | Majority label (if available) |
| `ASPECT_label_type` | `"gold"`, `"silver"`, or `"none"` |
---
## 🚀 Usage
You can load the datasets directly via 🤗 Datasets:
```python
from datasets import load_dataset
# Human annotations
human = load_dataset("boda/RevUtil_human")
# Synthetic annotations
synthetic = load_dataset("boda/RevUtil_synthetic")
```
## 📎 Citation
```
@inproceedings{sadallah-etal-2025-good,
title = "The Good, the Bad and the Constructive: Automatically Measuring Peer Review{'}s Utility for Authors",
author = {Sadallah, Abdelrahman and
Baumg{\"a}rtner, Tim and
Gurevych, Iryna and
Briscoe, Ted},
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1476/",
doi = "10.18653/v1/2025.emnlp-main.1476",
pages = "28979--29009",
ISBN = "979-8-89176-332-6"
}
``` |