@inproceedings{hoque-etal-2025-ciol,
title = "{CIOL} at {CLP}sych 2025: Using Large Lanuage Models for Understanding and Summarizing Clinical Texts",
author = "Hoque, Md. Iqramul and
Anik, Mahfuz Ahmed and
Wasi, Azmine Toushik",
editor = "Zirikly, Ayah and
Yates, Andrew and
Desmet, Bart and
Ireland, Molly and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ophir, Yaakov",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clpsych-1.19/",
doi = "10.18653/v1/2025.clpsych-1.19",
pages = "235--241",
ISBN = "979-8-89176-226-8",
abstract = "The increasing prevalence of mental health discourse on social media has created a need for automated tools to assess psychological wellbeing. In this study, we propose a structured framework for evidence extraction, well-being scoring, and summary generation, developed as part of the CLPsych 2025 shared task. Our approach integrates feature-based classification with context-aware language modeling to identify self-state indicators, predict well-being scores, and generate clinically relevant summaries. Our system achieved a recall of 0.56 for evidence extraction, an MSE of 3.89 in well-being scoring, and high consistency scores (0.612 post-level, 0.801 timeline-level) in summary generation, ensuring strong alignment with extracted evidence. With an overall good rank, our framework demonstrates robustness in social media-based mental health monitoring. By providing interpretable assessments of psychological states, our work contributes to early detection and intervention strategies, assisting researchers and mental health professionals in understanding online well-being trends and enhancing digital mental health support systems."
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<abstract>The increasing prevalence of mental health discourse on social media has created a need for automated tools to assess psychological wellbeing. In this study, we propose a structured framework for evidence extraction, well-being scoring, and summary generation, developed as part of the CLPsych 2025 shared task. Our approach integrates feature-based classification with context-aware language modeling to identify self-state indicators, predict well-being scores, and generate clinically relevant summaries. Our system achieved a recall of 0.56 for evidence extraction, an MSE of 3.89 in well-being scoring, and high consistency scores (0.612 post-level, 0.801 timeline-level) in summary generation, ensuring strong alignment with extracted evidence. With an overall good rank, our framework demonstrates robustness in social media-based mental health monitoring. By providing interpretable assessments of psychological states, our work contributes to early detection and intervention strategies, assisting researchers and mental health professionals in understanding online well-being trends and enhancing digital mental health support systems.</abstract>
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%0 Conference Proceedings
%T CIOL at CLPsych 2025: Using Large Lanuage Models for Understanding and Summarizing Clinical Texts
%A Hoque, Md. Iqramul
%A Anik, Mahfuz Ahmed
%A Wasi, Azmine Toushik
%Y Zirikly, Ayah
%Y Yates, Andrew
%Y Desmet, Bart
%Y Ireland, Molly
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ophir, Yaakov
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-226-8
%F hoque-etal-2025-ciol
%X The increasing prevalence of mental health discourse on social media has created a need for automated tools to assess psychological wellbeing. In this study, we propose a structured framework for evidence extraction, well-being scoring, and summary generation, developed as part of the CLPsych 2025 shared task. Our approach integrates feature-based classification with context-aware language modeling to identify self-state indicators, predict well-being scores, and generate clinically relevant summaries. Our system achieved a recall of 0.56 for evidence extraction, an MSE of 3.89 in well-being scoring, and high consistency scores (0.612 post-level, 0.801 timeline-level) in summary generation, ensuring strong alignment with extracted evidence. With an overall good rank, our framework demonstrates robustness in social media-based mental health monitoring. By providing interpretable assessments of psychological states, our work contributes to early detection and intervention strategies, assisting researchers and mental health professionals in understanding online well-being trends and enhancing digital mental health support systems.
%R 10.18653/v1/2025.clpsych-1.19
%U https://aclanthology.org/2025.clpsych-1.19/
%U https://doi.org/10.18653/v1/2025.clpsych-1.19
%P 235-241
Markdown (Informal)
[CIOL at CLPsych 2025: Using Large Lanuage Models for Understanding and Summarizing Clinical Texts](https://aclanthology.org/2025.clpsych-1.19/) (Hoque et al., CLPsych 2025)
ACL