Text Simplification Using T5 Model and BART Model

Text simplification is an important part of Natural Language Processing. It focuses on transforming complex text into simpler versions while preserving the meaning. This study examines the performance of two advanced models, T5 (Text-to-Text Transfer Transformer) and BART (Bidirectional and Auto-Regressive Transformers), in simplifying text. The models are trained on a dataset containing complex and simplified text pairs. Their performance is evaluated using BLEU, ROUGE, and SARI scores. The results show that both models perform well, but BART achieves better BLEU and ROUGE scores.