BioASQ Participants Area
BioASQ - Task MultiClinSum
Clinical content, such as medical records and case reports, is rapidly growing and written in multiple languages, not just English. These reports are often lengthy, making it difficult for domain experts to extract and track key clinical insights. Generative AI and Large Language Models (LLMs) have shown promise in summarizing such content, condensing detailed reports into shorter texts while preserving essential medical information. This highlights the urgent need to evaluate and benchmark clinical summarization methods across multilingual case reports.
Since clinical case reports share similarities with medical discharge summaries, findings from the MultiClinSum project are also relevant to broader clinical summarization tasks. The MultiClinSum dataset includes cases related to rare diseases and specialties like cardiology and rheumatology, offering valuable resources for ongoing clinical NLP efforts—particularly the BARITONE, DataTool4Heart, and AI4HF projects.
The MultiClinSum task focused on the automatic summarization of long clinical case reports written in multiple languages—specifically English, Spanish, French, and Portuguese. For evaluation, the automatically generated summaries were compared to human-written summaries using metrics such as ROUGE-2 and BERTScore.
Acknowledgements:
The BioASQ Task MultiCardioNER is co-ogranized with the Barcelona Supercomputing Center.
More information https://temu.bsc.es/multiclinsum/
