LitSumm: Large language models for literature summarisation of non-coding RNAs
This addresses the bottleneck of limited curator time in biomedical knowledgebases, though it is an incremental application of existing LLM methods to a specific domain.
The authors tackled the challenge of literature curation in RNA science by generating summaries for non-coding RNAs using large language models, achieving high-quality, factually accurate summaries as rated in manual assessments.
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. 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 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.