@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",
    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 [\url{https://github.com/tetsu9923/SciReviewGen}](\url{https://github.com/tetsu9923/SciReviewGen}).",
}

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 [\url{https://github.com/tetsu9923/SciReviewGen}](\url{https://github.com/tetsu9923/SciReviewGen}).
Kasanishi, Tetsu Isonuma, Masaru Mori, Junichiro Sakata, Ichiro
SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation