A survey of automated methods for biomedical text simplification. (9th September 2022)

• Record Type: Journal Article
• Authors: Ondov, Brian; Attal, Kush; Demner-Fushman, Dina
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 11(2022)
• Page Start: 1976
• Page End: 1988
• Publication Date: 2022-09-09
• Subjects: natural language processing -- machine learning -- text simplification -- plain language -- health literacy; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac149
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Plain language in medicine has long been advocated as a way to improve patient understanding and engagement. As the field of Natural Language Processing has progressed, increasingly sophisticated methods have been explored for the automatic simplification of existing biomedical text for consumers. We survey the literature in this area with the goals of characterizing approaches and applications, summarizing existing resources, and identifying remaining challenges. Materials and Methods: We search English language literature using lists of synonyms for both the task (eg, "text simplification") and the domain (eg, "biomedical"), and searching for all pairs of these synonyms using Google Scholar, Semantic Scholar, PubMed, ACL Anthology, and DBLP. We expand search terms based on results and further include any pertinent papers not in the search results but cited by those that are. Results: We find 45 papers that we deem relevant to the automatic simplification of biomedical text, with data spanning 7 natural languages. Of these (nonexclusively), 32 describe tools or methods, 13 present data sets or resources, and 9 describe impacts on human comprehension. Of the tools or methods, 22 are chiefly procedural and 10 are chiefly neural. Conclusions: Though neural methods hold promise for this task, scarcity of parallel data has led to continued development of procedural methods. Various low-resource mitigations have been proposed to advance neural methods, including paragraph-level and unsupervised models and augmentation of neural models with procedural elements drawing from knowledge bases. However, high-quality parallel data will likely be crucial for developing fully automated biomedical text simplification.