@inproceedings{el-khettari-etal-2025-summarization,
title = "Summarization for Generative Relation Extraction in the Microbiome Domain",
author = "El Khettari, Oumaima and
Quiniou, Solen and
Chaffron, Samuel",
editor = "Bechet, Fr{\'e}d{\'e}ric and
Chifu, Adrian-Gabriel and
Pinel-sauvagnat, Karen and
Favre, Benoit and
Maes, Eliot and
Nurbakova, Diana",
booktitle = "Actes de l'atelier Traitement du langage m{\'e}dical {\`a} l'{\'e}poque des LLMs 2025 (MLP-LLM)",
month = "6",
year = "2025",
address = "Marseille, France",
publisher = "ATALA {\textbackslash}{\textbackslash}{\&} ARIA",
url = "https://aclanthology.org/2025.jeptalnrecital-mlpllm.6/",
pages = "68--82",
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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="el-khettari-etal-2025-summarization">
<titleInfo>
<title>Summarization for Generative Relation Extraction in the Microbiome Domain</title>
</titleInfo>
<name type="personal">
<namePart type="given">Oumaima</namePart>
<namePart type="family">El Khettari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Solen</namePart>
<namePart type="family">Quiniou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samuel</namePart>
<namePart type="family">Chaffron</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Actes de l’atelier Traitement du langage médical à l’époque des LLMs 2025 (MLP-LLM)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Frédéric</namePart>
<namePart type="family">Bechet</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Adrian-Gabriel</namePart>
<namePart type="family">Chifu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Karen</namePart>
<namePart type="family">Pinel-sauvagnat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Benoit</namePart>
<namePart type="family">Favre</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eliot</namePart>
<namePart type="family">Maes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Diana</namePart>
<namePart type="family">Nurbakova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ATALA \textbackslash\textbackslash& ARIA</publisher>
<place>
<placeTerm type="text">Marseille, France</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">el-khettari-etal-2025-summarization</identifier>
<location>
<url>https://aclanthology.org/2025.jeptalnrecital-mlpllm.6/</url>
</location>
<part>
<date>2025-6</date>
<extent unit="page">
<start>68</start>
<end>82</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Summarization for Generative Relation Extraction in the Microbiome Domain
%A El Khettari, Oumaima
%A Quiniou, Solen
%A Chaffron, Samuel
%Y Bechet, Frédéric
%Y Chifu, Adrian-Gabriel
%Y Pinel-sauvagnat, Karen
%Y Favre, Benoit
%Y Maes, Eliot
%Y Nurbakova, Diana
%S Actes de l’atelier Traitement du langage médical à l’époque des LLMs 2025 (MLP-LLM)
%D 2025
%8 June
%I ATALA \textbackslash\textbackslash& ARIA
%C Marseille, France
%F el-khettari-etal-2025-summarization
%X 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.
%U https://aclanthology.org/2025.jeptalnrecital-mlpllm.6/
%P 68-82
Markdown (Informal)
[Summarization for Generative Relation Extraction in the Microbiome Domain](https://aclanthology.org/2025.jeptalnrecital-mlpllm.6/) (El Khettari et al., JEP/TALN/RECITAL 2025)
ACL