Sentence-Aligned Simplification of Biomedical Abstracts.

Brian Ondov, Dina Demner-Fushman
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Abstract

The availability of biomedical abstracts in online databases could improve health literacy and drive more informed choices. However, the technical language of these documents makes them inaccessible to healthcare consumers, causing disengagement, frustration and potential misuse. In this work we explore adapting foundation language models to the Plain Language Adaptation of Biomedical Abstracts benchmark. This task is challenging because it requires sentence-by-sentence simplifications, but entire abstracts must also be simplified cohesively. We present a sentence-wise autoregressive approach and report experiments with this technique in both zero-shot and fine-tuned settings, using both proprietary and open-source models. We also introduce a stochastic regularization technique to encourage recovery from source-copying during autoregressive inference. Our best-performing model achieves a 32 point increase in SARI and 6 point increase in BERTscore over the reported state-of-the-art. This also surpasses performance of recent open-domain and biomedical sentence simplification models on this task. Further, in manual evaluation, models achieve factual accuracy comparable to human-level, with simplicity close to that of humans. Abstracts simplified by these models could unlock a massive source of health information while retaining clear provenance for each statement to enhance trustworthiness.

生物医学摘要的句子对齐简化。
在线数据库中生物医学摘要的可用性可以提高健康素养并推动更明智的选择。然而,这些文档的技术语言使医疗保健消费者无法访问它们,从而导致脱离、沮丧和潜在的误用。在这项工作中,我们探索了将基础语言模型适应为生物医学摘要的普通语言适应基准。这项任务具有挑战性,因为它需要逐句简化,但整个摘要也必须连贯地简化。我们提出了一种句子智能自回归方法,并报告了在零射击和微调设置下使用该技术的实验,使用专有和开源模型。我们还引入了一种随机正则化技术,以鼓励在自回归推理期间从源复制中恢复。我们表现最好的模型在SARI中增加了32分,在BERTscore中增加了6分。这也超过了最近的开放领域和生物医学句子简化模型在这个任务上的表现。此外,在人工评估中,模型达到了与人类水平相当的事实准确性,简单性接近人类。通过这些模型简化的摘要可以解锁大量的健康信息来源,同时为每个陈述保留明确的来源,以提高可信度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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