Iterative Prompt Refinement for Dyslexia-Friendly Text Summarization Using GPT-4o

TL;DR

This study develops an iterative prompt-based summarization method using GPT-4o to generate dyslexia-friendly texts, achieving high readability and semantic quality on news articles, and establishes an empirical baseline for accessible NLP summarization.

Contribution

The paper introduces a novel iterative prompt refinement pipeline for dyslexia-friendly summarization using GPT-4o, demonstrating its effectiveness on a large news dataset.

Findings

Most summaries meet readability threshold within four attempts

Many summaries succeed on the first try

Stable composite scores indicate consistent quality

Abstract

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.