Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarization

In mental health counseling, condensing dialogues into concise and relevant summaries (aka counseling notes) holds pivotal significance.Large Language Models (LLMs) exhibit remarkable capabilities in various generative tasks; however, their adaptation to domainspecific intricacies remains challenging, especially within mental health contexts.Unlike standard LLMs, mental health experts first plan to apply domain knowledge in writing summaries.Our work enhances LLMs' ability by introducing a novel planning engine to orchestrate structuring knowledge alignment.To achieve high-order planning, we divide knowledge encapsulation into two major phases: (i) holding dialogue structure and (ii) incorporating domain-specific knowledge.We employ a planning engine on Llama-2, resulting in a novel framework, PIECE.Our proposed system employs knowledge filtering-cum-scaffolding to encapsulate domain knowledge.Additionally, PIECE leverages sheaf convolution learning to enhance its understanding of the dialogue's structural nuances.We compare PIECE with 14 baseline methods and observe a significant improvement across ROUGE and Bleurt scores.Further, expert evaluation and analyses validate the generation quality to be effective, sometimes even surpassing the gold standard.We further benchmark PIECE with other LLMs and report improvement, including Llama-2 (+2.72%),Mistral (+2.04%) and Zephyr (+1.59%), to justify the generalizability of the planning engine.Counseling Dialogue C: I'm done talking to all of you; it's a waste of time.You're all the same, offering nothing.T: You're saying we're all alike?C: Yes, everyone's the same, no solutions.How do I get better?I came here to fix things.T: Do you need a step-by-step plan?C: Yes, something tangible to grasp.T: It needs an organic process between us. C: I'm always intense,