2023/05/24 by Tetsu Kasanishi, Masaru Isonuma, Kasanishi, Tetsu +5 · 8 citations
Computer Science · Engineering · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Artificial intelligence #Automatic summarization #Computation and Language (cs.CL) #Computer science #Data science #Engineering #FOS: Computer and information sciences #Information retrieval #Machine learning #Natural language processing #Software Engineering Research #Task (project management) #Topic Modeling #Transformer
paper · pdf · doi:10.48550/arxiv.2305.15186
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.