@inproceedings{evgin-etal-2025-metninozu,
    title = "{M}etnin{O}z{U} at {B}io{L}ay{S}umm2025: Text Summarization with Reverse Data Augmentation and Injecting Salient Sentences",
    author = "Evgin, Egecan  and
      Karadeniz, Ilknur  and
      Y{\i}ld{\i}z, Olcay Taner",
    editor = "Soni, Sarvesh  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.bionlp-share.22/",
    doi = "10.18653/v1/2025.bionlp-share.22",
    pages = "179--184",
    ISBN = "979-8-89176-276-3",
    abstract = "In this paper, we present our approach to the BioLaySumm 2025 Shared Task on lay summarization of biomedical research articles, which was conducted as part of the BioNLP Workshop 2025. The aim of the task is to create lay summaries from scientific articles to improve accessibility for a non-expert audience. To this end, we applied preprocessing techniques to clean and standardize the input texts, and fine-tuned Qwen2.5 and Qwen3-based language models for the summarization task. For abstract-based fine-tuning, we investigated whether we can insert salient sentences from the main article into the summary to enrich the input. We also curated a dataset of child-friendly articles with corresponding gold-standard summaries and used large language models to rewrite them into more complex scientific variants to augment our training data with more examples."
}