Universal Readability: Simplification of Complex Text Using Deep Learning

The proposed framework implements text simplification through Bidirectional Encoder Representations from Transformers (BERT) model structures together with Generative Pre-trained Transformer-2 (GPT-2) deep learning elements. Traditional rule-based and statistical methods cannot accurately achieve the balance between simplification while retaining the original meaning. The evaluation results yielded a Bilingual Evaluation Understudy (BLEU) score of 0.95 while demonstrating superior readability performance. The research evaluation established successful text simplification because the Simple Measure of Gobbledygook (SMOG) reading difficulty decreased while the Flesch Reading Ease increased. The sophisticated technique delivers better outcomes than standard testing systems because it works efficiently with various types of text documents. The method benefits educational institutions and technical tasks with outstanding practicality and solves accessibility requirements effectively. Future research should integrate diverse data with domain-specific features to improve performance of the model.