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 "id": "taln-ls2n/ACL-rlg",
 "owner": "taln-ls2n",
 "name": "ACL-rlg",
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 "last_modified": "2026-02-17T13:38:21.000Z",
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 "citation": "@inproceedings{aubert-beduchaud-etal-2025-acl,\n    title = \"{ACL}-rlg: A Dataset for Reading List Generation\",\n    author = \"Aubert-B{\\'e}duchaud, Julien  and\n      Boudin, Florian  and\n      Daille, B{\\'e}atrice  and\n      Dufour, Richard\",\n    editor = \"Rambow, Owen  and\n      Wanner, Leo  and\n      Apidianaki, Marianna  and\n      Al-Khalifa, Hend  and\n      Eugenio, Barbara Di  and\n      Schockaert, Steven\",\n    booktitle = \"Proceedings of the 31st International Conference on Computational Linguistics\",\n    month = jan,\n    year = \"2025\",\n    address = \"Abu Dhabi, UAE\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.coling-main.327/\",\n    pages = \"4910--4919\",\n    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.\"\n}",
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