[2605.03103] MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
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Computer Science > Computation and Language
arXiv:2605.03103 (cs)
[Submitted on 4 May 2026]
Title:MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
Authors:Yingyun Li, Yu Wang, Haiyang Qian
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Abstract:Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical histories. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned question answering (QA), and (iii) end-to-end key-value pair extraction. However, existing evaluations often under-model two factors: heterogeneous and incompletely known key representations, and OCR-induced noise. This makes it difficult to assess model robustness in real-world settings.
We present MedStruct-S, a benchmark specifically designed to evaluate these tasks under unknown keys and OCR noise. MedStruct-S contains 3,582 annotated real-world clinical report pages. Using MedStruct-S, we benchmark two representative paradigms: encoder-only sequence labeling with post-processing and decoder-only structured generation, covering four encoder-only and five decoder-only models spanning 0.11B to 103B parameters. Our results show that encoder-only models achieve the best performance for non-null-value key-conditioned QA despite being substantially smaller than decoder-only models. When comparing models of similar order of magnitude, encoder-only models still perform better overall. Without controlling for model scale, fine-tuned decoder-only models deliver the strongest overall results. These findings show that the benchmark provides a reliable and practical basis for selecting and comparing models across different semi-structured IE settings.
Comments: 11 pages, 5 figures. Accepted by KSEM 2026. This is the author's preprint version; the final authenticated version will be available in the Springer LNCS/LNAI proceedings
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
ACM classes: I.2.7; H.3.1; J.3
Cite as: arXiv:2605.03103 [cs.CL]
(or arXiv:2605.03103v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.03103
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haiyang Qian [view email] [v1] Mon, 4 May 2026 19:37:21 UTC (7,126 KB)
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