@inproceedings{El-Khettari-Quiniou-Chaffron:CORIA-TALN:2025,
    author = "El Khettari, Oumaima and Quiniou, Solen and Chaffron, Samuel",
    title = "Summarization for Generative Relation Extraction in the Microbiome Domain",
    booktitle = "Actes de CORIA-TALN-RJCRI-RECITAL 2025. Actes de l'atelier Traitement du langage m\'edical \`a l{\textquoteright}\'epoque des LLMs 2025 (MLP-LLM)",
    month = "6",
    year = "2025",
    address = "Marseille, France",
    publisher = "Association pour le Traitement Automatique des Langues",
    pages = "68-82",
    note = "R\'esum\'e automatique pour l{\textquoteright}extraction g\'en\'erative de relations dans le domaine du microbiome",
    abstract = "We explore a generative relation extraction (RE) pipeline tailored to the study of interactions in the intestinal microbiome, a complex and low-resource biomedical domain. Our method leverages summarization with large language models (LLMs) to refine context before extracting relations via instruction-tuned generation. Preliminary results on a dedicated corpus show that summarization improves generative RE performance by reducing noise and guiding the model. However, BERT-based RE approaches still outperform generative models. This ongoing work demonstrates the potential of generative methods to support the study of specialized domains in low-resources setting.",
    keywords = "Generative Relation Extraction, Instruction-tuning, Low-Resource Domain, Microbiome.",
    url = "https://talnarchives.atala.org/ateliers/2025/MLP-LLM/183.pdf"
}