用GPT-4o迭代优化提示词，让阅读障碍者更易理解新闻摘要。
Iterative Prompt Refinement for Dyslexia-Friendly Text Summarization Using GPT-4o
- 基于GPT-4o设计迭代提示优化流程，逐轮提升摘要可读性。
- 90%以上摘要在四轮内达到Flesch阅读易度≥90的门槛，多数首试成功。
- 兼顾可读性与语义准确性的综合评分稳定在0.55左右，适合无障碍文本生成研究。
阅读障碍影响全球约10%的人群，在阅读流畅性和文本理解方面持续带来挑战。现有辅助技术多聚焦视觉呈现，但语言复杂性仍是实现公平获取的重大障碍。本文基于GPT-4o构建了一种迭代式提示优化管道，开展针对阅读障碍友好型文本摘要的实证研究。在约2,000篇新闻文章样本上评估，目标可读性为Flesch Reading Ease ≥ 90。结果表明，多数摘要在四次尝试内达标，部分甚至首次即成功。结合可读性与语义保真度的综合得分在0.13至0.73之间波动，典型值约为0.55。该研究为以可访问性为导向的NLP摘要任务建立了实证基准，并推动未来面向阅读障碍用户的以人为中心的评估。
原文摘要 · Abstract (English)
Dyslexia affects approximately 10% of the global population and presents persistent challenges in reading fluency and text comprehension. While existing assistive technologies address visual presentation, linguistic complexity remains a substantial barrier to equitable access. This paper presents an empirical study on dyslexia-friendly text summarization using an iterative prompt-based refinement pipeline built on GPT-4o. We evaluate the pipeline on approximately 2,000 news article samples, applying a readability target of Flesch Reading Ease >= 90. Results show that the majority of summaries meet the readability threshold within four attempts, with many succeeding on the first try. A composite score combining readability and semantic fidelity shows stable performance across the dataset, ranging from 0.13 to 0.73 with a typical value near 0.55. These findings establish an empirical baseline for accessibility-driven NLP summarization and motivate further human-centered evaluation with dyslexic readers.
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