@inproceedings{rebayet-etal-2026-cs,
    title = "{CS}{\_}{M}etro at {P}sy{D}ef{D}etect: Detecting Psychological Defense Mechanisms in Mental Health Dialogues with Summarization-Enhanced Transformer Ensembles",
    author = "Rebayet, Oarisa  and
      Walee, Radiul  and
      Shohan, Symom Hossain  and
      Ahmed, Kawsar  and
      Hoque, Mohammed Moshiul",
    editor = "Gupta, Deepak  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the {B}io{NLP} 2026 (Shared Tasks)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-2.26/",
    pages = "191--200",
    ISBN = "979-8-89176-435-4",
    abstract = "Detecting psychological defense mechanisms in supportive conversations is essential for assisting mental health practitioners. Natural language processing techniques are increasingly integral to such systems, enabling automated classification of defense levels to better understand help-seeker behavior and resistance patterns. In PsyDefDetect at BioNLP 2026, we address the task of nine-class defense level classification on the PSYDEFCONV corpus. We propose a three-stage pipeline combining LLM-based dialogue summarization, domain-specific transformer fine-tuning, and rule-based ensemble prediction. Additionally, we evaluate three mental health domain-specific transformers (Mental-BERT, Mental-RoBERTa, Mental-XLNet) alongside fine-tuned LLMs (Qwen3-4B, Qwen3-1.7B, Mistral-7B under different input conditions. Experimental results on the released test-set gold labels show that our ensemble approach achieves the best performance, reaching 34.69{\%} macro F1 and surpassing the baseline by 4.69 percentage points. On the official PsyDefDetect Leaderboard 1 (labels 1{--}8), the submitted system achieved a Macro-F1 score of 23.46{\%}, ranking 15th out of 21 teams, while on Leaderboard 2 (labels 0{--}8), it achieved 30.04{\%}, securing 14th place. These findings demonstrate that domain-specific transformers substantially outperform generic LLM fine-tuning on this specialized clinical task."
}