Iterative Prompt Refinement for Dyslexia-Friendly Text Summarization Using GPT-4o
This work addresses the significant challenge of linguistic complexity in text for dyslexic readers, who comprise about 10% of the global population, by providing an empirical baseline for accessibility-driven NLP summarization.
This paper explores dyslexia-friendly text summarization using an iterative prompt refinement pipeline with GPT-4o, aiming for a Flesch Reading Ease score of 90 or higher. The system successfully generated summaries meeting this readability target within four attempts for most of 2,000 news articles, with many succeeding on the first try.
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.