  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.
