提出五维可读性评估框架，让健康文本更易懂且可信
Toward Human-Centered Readability Evaluation
- 基于人机交互与健康传播理论构建五维评估框架
- 融合自动指标与结构化人工反馈，捕捉可读性深层特征
- 适合关注医疗文本可及性与用户体验的研究者
文本简化对提升公众健康信息可及性至关重要，尤其面向健康素养有限的人群。然而，当前NLP常用评估指标如BLEU、FKGL、SARI主要关注表层特征，无法衡量清晰度、可信度、语气、文化相关性和可操作性等以人为本的品质。这一缺陷在高风险健康语境中尤为突出，沟通必须不仅简单，还需可用、尊重且可信。为此，我们提出人类中心可读性评分（HCRS），一个基于人机交互与健康传播研究的五维评估框架。该框架结合自动度量与结构化人工反馈，捕捉可读性的关系性与情境性特征。我们阐述了该框架的设计，讨论其在参与式评估流程中的整合方式，并提出实证验证协议。本研究旨在推动健康文本简化的评估超越表面指标，使NLP系统更贴近多元用户的需求、期望与生活经验。
原文摘要 · Abstract (English)
Text simplification is essential for making public health information accessible to diverse populations, including those with limited health literacy. However, commonly used evaluation metrics in Natural Language Processing (NLP), such as BLEU, FKGL, and SARI, mainly capture surface-level features and fail to account for human-centered qualities like clarity, trustworthiness, tone, cultural relevance, and actionability. This limitation is particularly critical in high-stakes health contexts, where communication must be not only simple but also usable, respectful, and trustworthy. To address this gap, we propose the Human-Centered Readability Score (HCRS), a five-dimensional evaluation framework grounded in Human-Computer Interaction (HCI) and health communication research. HCRS integrates automatic measures with structured human feedback to capture the relational and contextual aspects of readability. We outline the framework, discuss its integration into participatory evaluation workflows, and present a protocol for empirical validation. This work aims to advance the evaluation of health text simplification beyond surface metrics, enabling NLP systems that align more closely with diverse users' needs, expectations, and lived experiences.
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