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Motivation: Curation of literature in life sciences is a growing challenge.The continued increase in the rate of publication, coupled with the relativelyfixed number of curators worldwide presents a major challenge to developers ofbiomedical knowledgebases. Very few knowledgebases have resources to scale tothe whole relevant literature and all have to prioritise their efforts.  Results: In this work, we take a first step to alleviating the lack ofcurator time in RNA science by generating summaries of literature fornon-coding RNAs using large language models (LLMs). We demonstrate thathigh-quality, factually accurate summaries with accurate references can beautomatically generated from the literature using a commercial LLM and a chainof prompts and checks. Manual assessment was carried out for a subset ofsummaries, with the majority being rated extremely high quality. We alsoapplied the most commonly used automated evaluation approaches, finding thatthey do not correlate with human assessment. Finally, we apply our tool to aselection of over 4,600 ncRNAs and make the generated summaries available viathe RNAcentral resource. We conclude that automated literature summarization isfeasible with the current generation of LLMs, provided careful prompting andautomated checking are applied.  Availability: Code used to produce these summaries can be found here:https://github.com/RNAcentral/litscan-summarization and the dataset of contextsand summaries can be found here:https://huggingface.co/datasets/RNAcentral/litsumm-v1. Summaries are alsodisplayed on the RNA report pages in RNAcentral (https://rnacentral.org/)

LitSumm: Large language models for literature summarisation of non-coding RNAs