Dyslexia is a learning disorder that causes difficulty in reading and identifying relations between words and letters (Catts et al. in Ann Dyslexia 74(3):282–302, 2024 [1]. To improve reading accessibility within dyslexic patients, this study aimed to develop models for summarization of documents as well as provide a streamlined method to convert documents into dyslexia-friendly versions. Within this study, several summarization models were tested to generate effective text summaries, while defining Python functions to convert regular text into dyslexia-friendly text. Models attempted with are: Term Frequency-Inverse Document Frequency Summarizer, Term Frequency-Inverse Document Frequency Summarizer with Support Vector Machine, and BART Transformer. After analysing the results, the BART Trained Summarization Model results are fruitful having a ROUGE R-1 F1 Score of 0.4510, a R-2 F1 Score of 0.2571 and a R-L F1 Score of 0.4177, ultimately successfully generating dyslexia-friendly summarized documents.