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.

This paper presents the Seed-CAT submission to the WMT24 Open Language Data Initiative shared task. We detail our data collection method, which involves a computer-aided translation tool developed explicitly for translating Seed corpora. We release a professionally translated Spanish corpus and a provenance dataset documenting the translation process. The quality of the data was validated on the FLORES+ benchmark with English-Spanish neural machine translation models, achieving an average chrF++ score of 34.9.