ACL-rlg: A Dataset for Reading List Generation
Julien Aubert-Béduchaud, Florian Boudin, Béatrice Daille, Richard Dufour
Abstract
Familiarizing oneself with a new scientific field and its existing literature can be daunting due to the large amount of available articles. Curated lists of academic references, or reading lists, compiled by experts, offer a structured way to gain a comprehensive overview of a domain or a specific scientific challenge. In this work, we introduce ACL-rlg, the largest open expert-annotated reading list dataset. We also provide multiple baselines for evaluating reading list generation and formally define it as a retrieval task. Our qualitative study highlights that traditional scholarly search engines and indexing methods perform poorly on this task, and GPT-4o, despite showing better results, exhibits signs of potential data contamination.
BibTeX
@inproceedings{aubert-beduchaud-etal-2025-acl,
title = "{ACL}-rlg: A Dataset for Reading List Generation",
author = "Aubert-B{\'e}duchaud, Julien and
Boudin, Florian and
Daille, B{\'e}atrice and
Dufour, Richard",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.327/",
pages = "4910--4919"
}