Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs. Despite notable progress, methods in medical simplification sometimes result in the generated text having lower quality and diversity. In this work, we explore ways to further improve the readability of text simplification in the medical domain. We propose (1) a new unlikelihood loss that encourages generation of simpler terms and (2) a reranked beam search decoding method that optimizes for simplicity, which achieve better performance on readability metrics on three datasets. This study's findings offer promising avenues for improving text simplification in the medical field.

翻译：文本简化已成为人工智能领域中日益重要的应用，旨在弥合医学等专业领域的沟通鸿沟——这些领域的词汇常以技术术语和复杂结构为主导。尽管已取得显著进展，但医学简化方法有时会导致生成的文本质量较低且多样性不足。本研究探索了进一步提升医学领域文本简化可读性的方法。我们提出：（1）一种新的非似然损失函数，用于鼓励生成更简单的术语；（2）一种针对简洁性优化的重排序束搜索解码方法。在三个数据集上的实验表明，所提方法在可读性指标上取得了更优表现。本研究成果为改善医学领域的文本简化提供了有前景的探索方向。