Simplified Text

Text simplification research focuses on automatically generating simpler versions of complex texts while preserving meaning, primarily to improve accessibility for diverse audiences, including those with intellectual disabilities or limited health literacy. Current efforts leverage large language models (LLMs) like BART and BERT, often employing fine-tuning, reinforcement learning, and novel pre-training strategies to enhance the generation of simpler words and sentences. Evaluation methods are evolving beyond simple metrics to incorporate human comprehension assessments and address challenges in cross-lingual simplification and the preservation of nuanced meaning in simplified texts. This work has significant implications for improving access to information across various domains, from healthcare and public policy to education.