Iterative Prompt Refinement for Dyslexia-Friendly Text Summarization Using GPT-4o
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| Format: | Preprint |
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2026
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| _version_ | 1866912927892635648 |
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| author | Bhojwani, Samay Kain, Swarnima Xu, Lisong |
| author_facet | Bhojwani, Samay Kain, Swarnima Xu, Lisong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_22524 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Iterative Prompt Refinement for Dyslexia-Friendly Text Summarization Using GPT-4o Bhojwani, Samay Kain, Swarnima Xu, Lisong Computation and Language Artificial Intelligence 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. |
| title | Iterative Prompt Refinement for Dyslexia-Friendly Text Summarization Using GPT-4o |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2602.22524 |