arxiv: 2603.19275 · v2 · submitted 2026-02-28 · 💻 cs.CL · cs.AI

Recognition: no theorem link

Improving Automatic Summarization of Radiology Reports through Mid-Training of Large Language Models

Mengxian Lyu , Cheng Peng , Ziyi Chen , Mengyuan Zhang , Jieting Li Lu , Yonghui Wu

Authors on Pith no claims yet

Pith reviewed 2026-05-15 18:16 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords radiology report summarizationmid-traininglarge language modelssubdomain adaptationROUGE-LRadGraph-F1few-shot learningclinical text adaptation

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The pith

Mid-training on radiology subdomain data yields superior LLM summarization of reports.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes and tests a mid-training method to adapt large language models for better summarization of radiology reports. By adding a step of training on subdomain clinical text after general clinical pre-training but before task fine-tuning, the resulting models achieve higher performance. This matters because it offers a way to improve summary accuracy and factuality without changing the base model architecture. The results also indicate stronger performance when only a few examples are available for fine-tuning. Experiments used large-scale clinical text for the adaptation stages and standard benchmarks for evaluation.

Core claim

Our experiments demonstrate that incorporating a subdomain mid-training phase between clinical pre-training and fine-tuning results in superior automatic summarization of radiology reports, as the GatorTronT5-Radio model achieves the highest ROUGE-L and RadGraph-F1 scores on OpenI and MIMIC-CXR compared to models trained without the mid-training step. This three-stage approach also improves few-shot learning and alleviates the cold start problem.

What carries the argument

The mid-training step that adapts a clinically pre-trained model to the radiology subdomain before summarization fine-tuning.

Load-bearing premise

The performance gains result directly from the inclusion of the mid-training step on subdomain data and not from unmeasured differences in data volume or training procedures.

What would settle it

Retraining the models with identical data volumes and configurations but omitting the mid-training step and finding no difference or worse performance on OpenI and MIMIC-CXR benchmarks would falsify the central claim.

read the original abstract

Automatic summarization of radiology reports is an essential application to reduce the burden on physicians. Previous studies have widely used the "pre-training, fine-tuning" strategy to adapt large language models (LLMs) for summarization. This study proposed a subdomain adaptation through a mid-training method to improve summarization. We explored three adaptation strategies: (1) general-domain pre-training, (2) clinical-domain pre-training, and (3) clinical-domain pre-training followed by subdomain mid-training. We developed models using large-scale clinical text from the University of Florida (UF) Health and conducted mid-training and fine-tuning experiments using widely used benchmark datasets including OpenI and MIMIC-CXR. The experimental results show that the mid-trained model, GatorTronT5-Radio, achieved the best performance, outperforming models without mid-training in both text-based measures (ROUGE-L) and factuality measures (RadGraph-F1). Our mid-training methods also demonstrate better few-shot learning and could alleviate the "cold start" problem reported in previous studies as a learning barrier. Our findings support the use of "pre-training, mid-training, fine-tuning," instead of the widely used direct fine-tuning strategy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Mid-training on subdomain radiology data after clinical pre-training lifts ROUGE-L and RadGraph-F1 on OpenI and MIMIC-CXR while improving few-shot behavior.

read the letter

The main point is that adding a mid-training stage on radiology-specific text produces the best results among the three paths tested. Their GatorTronT5-Radio model beats the general-pretrain and clinical-pretrain versions on both text overlap and factuality metrics, and the setup appears to keep the mid-training step as the main variable across runs. The work also flags better few-shot learning, which matters when labeled radiology summaries are scarce. The three-way comparison using UF Health data for the earlier stages and standard benchmarks for fine-tuning keeps the experiment practical and tied to real clinical text volumes. That staged recipe is the concrete contribution here, and it directly targets the cold-start problem noted in prior work. The comparison is incremental rather than a new framework, but the gains are reported consistently enough to be worth checking. The abstract is light on exact data sizes, training steps, and significance tests, so the full paper needs to show those controls and any ablation runs to confirm the lift comes from mid-training rather than hidden differences in scale or data mix. If those details hold up, the attribution looks clean. No circular claims or unfalsifiable steps show up in the description. This is for teams working on clinical LLM adaptation or radiology NLP tools. Readers who need deployable training sequences for high-volume report summarization will get the most out of it. It deserves a serious referee because the empirical path is straightforward, the problem is real, and the controls isolate the variable of interest.

Referee Report

1 major / 2 minor

Summary. The paper proposes subdomain adaptation of LLMs for radiology report summarization via a mid-training step. It compares three strategies—general-domain pre-training, clinical-domain pre-training, and clinical pre-training followed by subdomain mid-training on UF Health data—then fine-tunes on OpenI and MIMIC-CXR. The mid-trained GatorTronT5-Radio model is reported to outperform the others on ROUGE-L and RadGraph-F1 while also improving few-shot learning and alleviating cold-start issues.

Significance. If the empirical gains hold under proper controls, the work demonstrates that an explicit mid-training phase on subdomain clinical text can improve both textual overlap and factual consistency in medical summarization, providing a practical alternative to direct fine-tuning and addressing data-scarcity barriers in clinical NLP.

major comments (1)

[Results] Results section: the central claim that GatorTronT5-Radio outperforms models without mid-training rests on ROUGE-L and RadGraph-F1 gains, yet the abstract supplies no numerical scores, baseline values, ablation tables, or statistical tests; without these the attribution to the mid-training step cannot be verified.

minor comments (2)

[Abstract] Abstract: expand to include at least the key quantitative improvements and dataset sizes for immediate verifiability.
[Methods] Methods: clarify the exact composition and scale of the UF Health mid-training corpus and any hyperparameter differences across the three strategies.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the major comment below and have revised the manuscript to strengthen the presentation of results and ensure the attribution to mid-training is clearly verifiable.

read point-by-point responses

Referee: [Results] Results section: the central claim that GatorTronT5-Radio outperforms models without mid-training rests on ROUGE-L and RadGraph-F1 gains, yet the abstract supplies no numerical scores, baseline values, ablation tables, or statistical tests; without these the attribution to the mid-training step cannot be verified.

Authors: We agree that the abstract should explicitly report key numerical results to support the claims. In the revised version, we have updated the abstract to include the specific ROUGE-L and RadGraph-F1 scores for GatorTronT5-Radio versus the general-domain and clinical-domain baselines. The results section already contains the full ablation tables, baseline comparisons, and per-metric breakdowns on both OpenI and MIMIC-CXR; we have now added paired statistical significance tests (t-tests with p-values) to quantify the gains attributable to the mid-training step. These changes make the evidence for the mid-training benefit fully transparent without altering any experimental outcomes. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper reports an empirical comparison of three LLM adaptation strategies (general pre-training, clinical pre-training, and clinical pre-training plus subdomain mid-training) on radiology report summarization. Performance is measured directly via ROUGE-L and RadGraph-F1 on standard benchmarks (OpenI, MIMIC-CXR) with no equations, fitted parameters, or derivation steps. The central claim attributes gains to the isolated mid-training variable under the stated experimental controls; no self-citation chain, ansatz, or renaming reduces any result to its own inputs by construction. This is a standard empirical ML paper whose validity rests on external benchmarks rather than internal definitional closure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations or new entities introduced. The claim rests on the empirical assumption that the chosen benchmarks and automatic metrics (ROUGE-L, RadGraph-F1) adequately reflect clinical utility and that the UF Health corpus is representative of the target subdomain.

pith-pipeline@v0.9.0 · 5528 in / 987 out tokens · 50897 ms · 2026-05-15T18:16:56.041700+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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