@inproceedings{cuadron-cortes-etal-2025-arghitz,
title = "{A}rg{H}i{TZ} at {A}rch{EHR}-{QA} 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality",
author = "Cuadron Cortes, Adrian and
Sagasti, Aimar and
Urruela, Maitane and
De La Iglesia, Iker and
Garc{\'i}a Domingo-aldama, Ane and
Atutxa Salazar, Aitziber and
Goikoetxea, Josu and
Barrena, Ander",
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://aclanthology.org/2025.bionlp-share.1/",
doi = "10.18653/v1/2025.bionlp-share.1",
pages = "1--10",
ISBN = "979-8-89176-276-3",
abstract = "This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text{---}by prompt or similarity ranking{---}, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cuadron-cortes-etal-2025-arghitz">
<titleInfo>
<title>ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality</title>
</titleInfo>
<name type="personal">
<namePart type="given">Adrian</namePart>
<namePart type="family">Cuadron Cortes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aimar</namePart>
<namePart type="family">Sagasti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maitane</namePart>
<namePart type="family">Urruela</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iker</namePart>
<namePart type="family">De La Iglesia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ane</namePart>
<namePart type="family">García Domingo-aldama</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aitziber</namePart>
<namePart type="family">Atutxa Salazar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Josu</namePart>
<namePart type="family">Goikoetxea</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ander</namePart>
<namePart type="family">Barrena</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sarvesh</namePart>
<namePart type="family">Soni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dina</namePart>
<namePart type="family">Demner-Fushman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-276-3</identifier>
</relatedItem>
<abstract>This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text—by prompt or similarity ranking—, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality.</abstract>
<identifier type="citekey">cuadron-cortes-etal-2025-arghitz</identifier>
<identifier type="doi">10.18653/v1/2025.bionlp-share.1</identifier>
<location>
<url>https://aclanthology.org/2025.bionlp-share.1/</url>
</location>
<part>
<date>2025-08</date>
<extent unit="page">
<start>1</start>
<end>10</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality
%A Cuadron Cortes, Adrian
%A Sagasti, Aimar
%A Urruela, Maitane
%A De La Iglesia, Iker
%A García Domingo-aldama, Ane
%A Atutxa Salazar, Aitziber
%A Goikoetxea, Josu
%A Barrena, Ander
%Y Soni, Sarvesh
%Y Demner-Fushman, Dina
%S Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-276-3
%F cuadron-cortes-etal-2025-arghitz
%X This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text—by prompt or similarity ranking—, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality.
%R 10.18653/v1/2025.bionlp-share.1
%U https://aclanthology.org/2025.bionlp-share.1/
%U https://doi.org/10.18653/v1/2025.bionlp-share.1
%P 1-10
Markdown (Informal)
[ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality](https://aclanthology.org/2025.bionlp-share.1/) (Cuadron Cortes et al., BioNLP 2025)
ACL
- Adrian Cuadron Cortes, Aimar Sagasti, Maitane Urruela, Iker De La Iglesia, Ane García Domingo-aldama, Aitziber Atutxa Salazar, Josu Goikoetxea, and Ander Barrena. 2025. ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality. In Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks), pages 1–10, Vienna, Austria. Association for Computational Linguistics.