All datasets / taln-ls2n
taln-ls2n/ACL-rlg
The dataset card's licence name or licence section contains wording that suggests non-commercial, research-only or academic-only use restrictions.
- Declared licence
- Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at a74a620
- Licence files
- No licence file at the top level of the repositoryEvidence: file list at a74a620
- Declared sources
- None listed in the card metadata
- Access gated on Hugging Face
- No
Citation text from the card
@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",
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."
}
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dataset_licence({ "id": "taln-ls2n/ACL-rlg" })
signal: "restrictive"
stated_licence: null
sources: 0