Authors - Soham Kulkarni, Suhani Thakur, Twsha Vyass, V. Yasaswini, Pooja Kamat Abstract - Dyslexia is a learning disorder that causes difficulty in reading and identifying relations between words and letters.[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.
