Medical text simplification: bridging the gap between medical research and public understanding

Abstract

Text Simplification is a subdomain of Natural Language Processing that focuses on applying computational techniques to modify the content and structure of the text to make it interpretable while retaining the main idea. The advancements in text simplification research have provided valuable benefits to a wide range of readers, including those with learning disabilities and non-native speakers. Moreover, even regular readers who are not experts in fields such as medicine or finance have found text simplification techniques to be useful in accessing scientific literature and research. This thesis aims to create a text simplification approach that can effectively simplify complex biomedical literature. Chapter 2 provides an insightful overview of the datasets, methods, and evaluation techniques used in text simplification. Chapter 3 conducts an extensive bibliometric analysis of literature in the field of text simplification to understand research trends, find important research and application topics of text simplification research, and understand shortcomings in the field. Based on the findings in Chapter 3, we found that the advancements in text simplification research can have a positive impact on the medical domain. The research in the field of medicine is constantly developing and contains important information about drugs and treatments for various life threatening diseases. Although this information is accessible to the public, it is very complex in nature, thus making it difficult to understand.