Better Late Than Never: Model-Agnostic Hallucination Post-Processing Framework Towards Clinical Text Summarization (2024)

Abstract: Clinical text summarization has proven successful in generating concise and coherent summaries. However, these summaries may include unintended text with hallucinations, which can mislead clinicians and patients. Existing methods for mitigating hallucinations can be categorized into task-specific and task-agnostic approaches. Task-specific methods lack versatility for real-world applicability. Meanwhile, task-agnostic methods are not model-agnostic, so they require retraining for different models, resulting in considerable computational costs. To address these challenges, we propose MEDAL, a model-agnostic framework designed to post-process medical hallucinations. MEDAL can seamlessly integrate with any medical summarization model, requiring no additional computational overhead. MEDAL comprises a medical infilling model and a hallucination correction model. The infilling model generates non-factual summaries with common errors to train the correction model. The correction model is incorporated with a self-examination mechanism to activate its cognitive capability. We conduct comprehensive experiments using 11 widely accepted metrics on 7 baseline models across 3 medical text summarization tasks. MEDAL demonstrates superior performance in correcting hallucinations when applied to summaries generated by pre-trained language models and large language models.

## introduction given the widespread use of electronic health records (ehr) (e.g., health questions, radiology reports, and doctor-patient dialogues), medical text summarization enhances healthcare efficiency @xcite . however, manual summarization is notably laborious and time-consuming @xcite , leading to potential errors under figure 1 : a radiology report with its reference summary and an erroneous summary generated by a summarization model. intrinsic errors are highlighted in red, signifying sections that should remain "unchanged" and "hemothorax". extrinsic errors are highlighted in green, indicating the issues of imposing causality and introducing additional details. however, clinical text summarization may engender hallucinations, resulting in intrinsic hallucinations (facts that contradict the source text) or extrinsic hallucinations (facts that cannot be directly inferred from the source text) @xcite , as illustrated in figure 1 . hallucinations may mislead clinicians and patients, posing significant medical risks. mitigating hallucinations is imperative for the seamless integration of automated summarization into clinical workflows. numerous methods have been proposed to address hallucinations in general summarization tasks @xcite . however, these methods lack tailored designs for medical contexts, limiting their direct application to the medical domain. recently, specific techniques for tackling hallucinations in medical summarization have been introduced. @xcite suggest selectively rewrite unsupported reference sentences to improve the training set. meanwhile, @xcite and @xcite focus on enhancing summarization models via reinforcement learning and knowledge-guided multi-objective optimization, respectively. nevertheless, these methods are unsuitable for clinical deployment as they are confined to a specific type of medical text. to address this challenge, @xcite introduce a finetuning strategy with wide applicability. however, its contrastive learning component demands highquality training data and substantial computational costs. moreover, the rise of large language models (llms) escalates fine-tuning costs due to the increasing model parameters. we propose medal, a model-agnostic postprocessing framework designed to address medical hallucinations in various summarization tasks. medal comprises two components: 1) a hallucination correction model for post-processing, and 2) a medical infilling model to generate training data for the correction model. the hallucination correction model is built upon flan-t5 @xcite , a model fine-tuned with instructions. our hallucination correction model fully leverages the cognitive capabilities of flan-t5 through selfexamination. the model is guided to examine hallucinations in a model-generated summary and then correct the summary based on its self-examination. specifically, we introduce a self-examination token that triggers the hallucination correction model to refine its generation process. furthermore, we employ the medical infilling model to create nonfactual summaries with errors aligned with those found in model-generated summaries. this approach aims to enhance the correction model's capacity to rectify common errors in automated medical summaries. specifically, we enhance a traditional infilling model @xcite by constructing medical cloze questions. these medical cloze questions help incorporate medical knowledge into the infilling model during training and are utilized to generate non-factual summaries during inference. we conduct extensive experiments to evaluate the performance of medal across three tasks: health question summarization, radiology report summarization, and doctor-patient dialogue summarization. first, medal has consistently achieved optimal or near-optimal performance using widely accepted general quality metrics and faithfulness metrics across three medical text sum-marization tasks. second, medal effectively rectifies hallucinations in summaries generated by llms, demonstrating its potential for future application. third, we demonstrate medal's robustness with the imbalanced training set. finally, further studies are conducted to comprehensively explain the effectiveness of self-examination. health question summarization is introduced by @xcite . recent models have achieved performance improvements using reinforcement learning @xcite , contrastive learning @xcite , transfer learning @xcite , multi-task learning @xcite , and question-aware transformer models @xcite . for radiology report summarization, most prior works leverage pre-extracted knowledge like ontologies and word graphs @xcite @xcite . @xcite and @xcite advocate for contrastive learning and reinforcement learning, respectively. recently, attention has shifted towards employing llms @xcite . doctor-patient dialogue summarization is early studied with pointer generator networks @xcite . @xcite explore summarization methods, while @xcite focus on the few-shot setting. @xcite focus on handling long conversations by fine-tuning bart. meddialog @xcite and aci-bench @xcite introduce datasets to advance the research. differing from the task-specific methods mentioned above, we propose a model-agnostic framework suitable for all these tasks, showcasing consistent strong performance across these tasks. ## method in this section, we first provide an overview of our proposed framework medal ( §3.1). sequentially, we detail the construction of medical cloze questions ( §3.2). finally, we delve into the architectures of the medical infilling model ( §3.3) and the hallucination correction model ( §3.4). ## results and analysis in this section, we aim to answer the following research questions: • rq1: does medal perform well across different medical text summarization tasks on general quality metrics and faithfulness metrics ( §5.1)? • rq2: does the self-examination process activate medal's cognitive capability ( §5.2), and if affirmative, what contributes to its efficacy ( §5.4)? • rq3: does medal exhibit robustness when trained with imbalanced samples ( §5.3)? ## conclusion in this paper, we propose medal, a modelagnostic post-processing framework designed to rectify hallucinations in medical summaries. medal comprises a medical infilling model and a hallucination correction model. the correction model is trained using synthetic datasets generated by the infilling model. a self-examination mechanism is incorporated into the correction model to enhance its performance. experimental results demonstrate medal's superiority across 1002 11 metrics in 3 medical text summarization tasks. medal is model-agnostic and can post-process summaries to enhance their reliability. furthermore, medal exhibits potential in rectifying summaries generated by llms, highlighting its prospective application in the era of llms.