mlsanityData to 14 Oct 2026

ProtoTopic: Prototypical Network for Few-Shot Medical Topic Modeling

arXiv 2510.13542 ↗First posted 15 Oct 2025cs.LGcited by 0

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Abstract (arXiv)

Topic modeling is a useful tool for analyzing large corpora of written documents, particularly academic papers. Despite a wide variety of proposed topic modeling techniques, these techniques do not perform well when applied to medical texts. This can be due to the low number of documents available for some topics in the healthcare domain. In this paper, we propose ProtoTopic, a prototypical network-based topic model used for topic generation for a set of medical paper abstracts. Prototypical networks are efficient, explainable models that make predictions by computing distances between input datapoints and a set of prototype representations, making them particularly effective in low-data or few-shot learning scenarios. With ProtoTopic, we demonstrate improved topic coherence and diversity compared to two topic modeling baselines used in the literature, demonstrating the ability of our model to generate medically relevant topics even with limited data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 15 Oct 2025.

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26 of 44 references.

References · 26

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14 more
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