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.