BioASQ Participants Area

BioASQ - Task MultiClinSum-2

Task MultiClinSum-2 will rely on a corpus of manually selected full clinical case reports and their corresponding clinical case report summaries derived from case report publications written in the previously mentioned languages. For evaluation proposes, automatically generated summaries will be compared against manually generated summaries generated by the original authors, exploring Rouge-2 scores and BERTScore for evaluation assessment. You can join anytime from March, 2026 onwards.

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. In addition, new languages such as Italian, Swedish, or Czech are being considered for addition, further broadening the linguistic coverage of task MultiClinSum-2. For evaluation, the automatically generated summaries were compared to human-written summaries using metrics such as ROUGE-2 and BERTScore.

Acknowledgements: