Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis (2023)

Abstract: Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting. Previous work aimed to enhance content selection and factuality through guided abstractive summarization. However, two key issues persist. First, current methods heavily rely on domain-specific resources to extract the guidance signal, limiting their transferability to domains and languages where those resources are unavailable. Second, while automatic metrics like ROUGE show progress, we lack a good understanding of the errors and failure modes in this task. To bridge these gaps, we first propose a domain-agnostic guidance signal in form of variable-length extractive summaries. Our empirical results on two English benchmarks demonstrate that this guidance signal improves upon unguided summarization while being competitive with domain-specific methods. Additionally, we run an expert evaluation of four systems according to a taxonomy of 11 fine-grained errors. We find that the most pressing differences between automatic summaries and those of radiologists relate to content selection including omissions (up to 52%) and additions (up to 57%). We hypothesize that latent reporting factors and corpus-level inconsistencies may limit models to reliably learn content selection from the available data, presenting promising directions for future work.

## introduction the radiology report is an important tool for radiologists to communicate examination results with other clinicians. typically, these reports contain three sections: the background section describing the exam and patient context, the findings section providing a detailed description of observations, and the impression section, which concisely summarizes the key findings @xcite . in the clinical process, the impression is of high importance as it informs further treatments. however, background: technique: chest, ap and lateral. comparison: _ and _. history: weakness and decreased blood sugar with leg swelling and tenderness. findings: the patient is status post coronary artery bypass graft surgery and apparently mitral valve replacement. the heart is mildly enlarged. the mediastinal and hilar contours appear unchanged. there is a slight interstitial abnormality, suggestive of a state of very mild congestion, but no new focal opacity. a left-sided pleural effusion has resolved although mild scarring or atelectasis persists. bones are probably demineralized. impression: findings suggesting mild pulmonary congestion. resolution of small left-side pleural effusion. ## method we formulate the task of summarizing radiology reports as follows. given the findings section of a report, represented as a sequence of tokens @xmath0, x 2 , . . . , x n ), the goal is to generate an impression @xmath1, y 2 , . . . , y m ) that accurately summarizes the most significant findings. the guided summarization framework extends this problem setting with an additional input signal @xmath2, g 2 , . . . , g l ) which aims to improve the quality of generated summaries by indicating salient information in x. ## technical evaluation rq1. to what extent are extractive summaries an effective guidance signal for radiology report summarization? rq2. how does adapting the extractive guidance length to each report impact the overall quality of summaries? ## conclusion in this work, we revisited guided abstractive summarization of radiology reports. we demonstrated that extractive summaries can be an effective guidance signal for the task, if we allow the length of this guidance signal to vary across reports, and thereby make the gap between domain-agnostic and domain-specific guidance smaller. furthermore, through a fine-grained error analysis of unguided and guided models we found that guidance successfully steers the content of summaries but that significant deficits in content selection persist. we hope that this paper motivates future efforts on content selection mechanisms for radiology report summarization, their evaluation in other domains and languages, and on more comprehensive evaluation suites. we release our error annotations which can serve as a starting point for evaluating the efficacy of metrics in capturing these errors.