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arXiv · 2506.08647

Summarization for Generative Relation Extraction in the Microbiome Domain

Oumaima El Khettari·Solen Quiniou·Samuel Chaffron
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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.

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Oumaima El Khettari, Solen Quiniou, Samuel Chaffron. 2025-06-10. Summarization for Generative Relation Extraction in the Microbiome Domain. https://arxiv.org/abs/2506.08647

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