Natural Language Processing-Driven Text Simplification System for Enhanced Readability

This paper suggests a Natural Language Processingbased Text Simplification System, which improves readability via a neural text simplification system that is Transformer-based, which is applied to a Transformer-based text simplification tool called T5 (Text-to-Text Transfer Transformer), which runs on the Hugging Face platform. The chosen approach characterizes simplification as a sequence-to-sequence translation task that is managed, i.e. simplification of complex English sentences into simpler ones without loss of significance. The system does joint lexical, syntactic, and semantic simplifications with the help of the pretrained T5 model being fine-tuned on parallel complex-simple corpora. The model smartly replaces the hard words, rearranges the long sentences and produces fluency and context-specific simplified outputs. The tool aids in scaling deployment, (automatic) evaluation based on such metrics as SARI, Flesch-Kincaid Grade Level, and domain customization to education, healthcare, and public communication. Findings indicate that T5-based method offers better readability enhancement at the cost of little information, hence a strong method of text simplification with automated tasks.