Automated Lay Language Summarization of Biomedical Scientific Reviews

Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon the complex structure of professional language this domain make information especially hard to interpret. Thus, there is an urgent unmet need for automated methods enhance accessibility biomedical literature general population. This problem can be framed type translation between healthcare professionals, that public. In paper, we introduce novel task generation lay summaries scientific reviews, construct dataset support development evaluation through which literature. We conduct analyses various challenges performing task, including not only summarization key points but also explanation background knowledge simplification language. experiment with state-of-the-art models well several data augmentation techniques, evaluate their performance using both metrics human assessment. Results indicate automatically generated produced contemporary neural architectures achieve promising quality readability compared reference developed public by experts (best ROUGE-L 50.24 Flesch-Kincaid score 13.30). discuss limitations current effort, providing insights directions future work.