SUWMIT at BioLaySumm2025: Instruction-based Summarization with Contrastive Decoding

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.