Year: 2025
Type: conference-paper
Abstract: In the following paper, we present our team's approach to subtask 1.1 of the BioLaySumm 2025 shared task, which entails the automated generation of lay summaries from biomedical articles.To this end, we experiment with a variety of methods for text preprocessing, extractive summarization, model fine-tuning, and abstractive summarization.Our final results are generated on a fine-tuned Llama 3.1 Instruct (8B) model, notably achieving top scores on two out of four relevance metrics, as well as the highest overall ranking among this year's participating teams on the plain lay summarization subtask.
Authors: Priyam Basu, Jose Cols, Daniel Jarvis, Yongsin Park, Daniel Rodabaugh
Institution: University of Washington
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: University of Washington
OpenAlex ID: W4412889083