Year: 2025
Type: conference-paper
Abstract: As part of the BioLaySumm shared task at ACL 2025, we developed a summarization tool designed to translate complex biomedical texts into layperson-friendly summaries.Our goal was to enhance accessibility and comprehension for patients and others without specialized medical knowledge.The system employed an extractive-then-abstractive summarization pipeline.For the abstractive component, we experimented with two models: Pegasus-XSum and a Falcons.aimodel pre-trained on medical data.Final outputs were evaluated using the official BioLaySumm 2025 metrics.To promote practical accessibility, we completed all experimentation on consumer-grade hardware, demonstrating the feasibility of our approach in low-resource settings.
Authors: Jen Wilson, Michael Pollack, Rachel Edwards, Avery Bellamy, Helen Salgi
Institution: University of Washington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412889089