SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation
Tetsu Kasanishi, Masaru Isonuma, Junichiro Mori, Ichiro Sakata
Abstract
Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation, the absence of large-scale datasets has been a stumbling block to the progress. We release SciReviewGen, consisting of over 10,000 literature reviews and 690,000 papers cited in the reviews. Based on the dataset, we evaluate recent transformer-based summarization models on the literature review generation task, including Fusion-in-Decoder extended for literature review generation. Human evaluation results show that some machine-generated summaries are comparable to human-written reviews, while revealing the challenges of automatic literature review generation such as hallucinations and a lack of detailed information. Our dataset and code are available at [https://github.com/tetsu9923/SciReviewGen](https://github.com/tetsu9923/SciReviewGen).
BibTeX
@inproceedings{kasanishi-etal-2023-scireviewgen,
title = "{S}ci{R}eview{G}en: A Large-scale Dataset for Automatic Literature Review Generation",
author = "Kasanishi, Tetsu and
Isonuma, Masaru and
Mori, Junichiro and
Sakata, Ichiro",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.418/",
doi = "10.18653/v1/2023.findings-acl.418",
pages = "6695--6715"
}