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