APhyND — An algorithmic framework for automatic narrative detection in psychotherapy session transcripts

The narratives expressed by patients during psychotherapy sessions provide a cognitive framework through which they process their experiences. While these narratives offer therapists valuable insights, automatically detecting them presents unique challenges due to their unstructured nature, parallel storytelling patterns, and frequent interruptions - characteristics distinct from narrative detection in traditional domains like news or social media. This paper first establishes a formal definition of therapeutic narratives as continuous segments containing characters, actions, and consequent changes, situated in specific temporal–spatial contexts. Working with domain experts, we developed and validated annotation guidelines achieving high inter-annotator agreement on a comprehensive corpus of Hebrew psychotherapy sessions. Building on this foundation, we introduce APhyND, a novel layered framework specifically designed for automatic narrative detection in psychotherapy session transcripts. Our framework makes several key innovations: (1) a clinically-informed architecture incorporating therapist-validated narrative criteria and specialized feature extraction for therapeutic discourse (2) integration of deep contextual understanding through BERT with sequence modeling via Conditional Random Fields, specifically adapted for therapeutic discourse, and (3) the first comprehensive solution for narrative detection in Hebrew psychotherapy transcripts, addressing the challenges of morphologically rich languages in therapeutic contexts. The framework employs a clinically-informed architecture incorporating therapist-validated narrative criteria, specialized feature extraction for therapeutic discourse, and adaptive threshold adjustment to handle inherent class imbalance. To evaluate the framework's effectiveness, we conducted experiments on a dataset of 38,434 sentences from 79 psychotherapy sessions in Hebrew. Our results demonstrate significant improvements over existing approaches, achieving an f1-score of 0.804, with particularly strong performance in handling interrupted narratives and speaker transitions. The framework's ability to process full therapy sessions in real-time while maintaining high accuracy makes it particularly valuable for clinical applications, addressing a critical gap in automated psychotherapy analysis tools.