Discourse-Guided Summarisation of Psychotherapy Dialogues via Graph-Fused Language Models.

Abstract

Psychotherapy summarisation poses unique challenges due to the emotional nuance, relational complexity, and non-linear discourse of therapeutic dialogues. Existing large language models (LLMs), even when fine-tuned, often fail to capture these structures, leading to clinically incomplete or incoherent summaries. In this work, we propose TheraFuse, a clinically grounded summarisation framework that adapts discourse-level relational graphs and LLaMA fusion to the psychotherapy domain. Our method first parses therapy dialogues into discourse graphs and encodes them using Relational Graph Convolutional Networks. These representations are then injected into LLaMA's hidden layers to guide generation. Experiments on a benchmark counselling dataset (iHOPE), following a benchmark counselling summary template (iCARE), show that TheraFuse significantly outperforms both zero-shot and fine-tuned baselines in both surface metrics (BLEU, ROUGE) and semantic alignment (BERTScore, InfoLM); these gains are statistically significant under paired $t$-tests with Holm-Bonferroni correction ($p< 0.05$, Section  VI-A.2). Human evaluation by mental health professionals confirms the improved coherence, structure, and clinical usefulness of the generated summaries.