Text simplification

Abstract

Understanding complex biomedical information often requires specialized expertise, creating barriers for non-experts seeking access to crucial knowledge. This research focuses on the development of a biomedical text simplification system employing deep learning methodologies. The intricacies of biomedical literature present formidable comprehension challenges for non-experts, primarily due to technical jargon and complex structures. The proposed system aims to overcome these hurdles by leveraging advanced deep learning techniques to decode and simplify complex texts. By enhancing the accessibility of biomedical information, the system endeavors to bridge the comprehension gap between domain experts and non-specialists. Initial progress in model development and preliminary evaluations demonstrate promising potential for simplifying technical language and structures. The ongoing research aims to refine and optimize the system, ultimately contributing to improved accessibility and understanding of critical biomedical information for a diverse audience.