Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study

Understand

Comprehensive summaries of sessions enable an effective continuity in mental health counseling, facilitating informed therapy planning.

• Yet, manual summarization presents a significant challenge, diverting experts' attention from the core counseling process.
• This study evaluates the effectiveness of state-of-the-art Large Language Models (LLMs) in selectively summarizing various components of therapy sessions through aspect-based summarization, aiming to benchmark their performance.
• We introduce MentalCLOUDS, a counseling-component guided summarization dataset consisting of 191 counseling sessions with summaries focused on three distinct counseling components (aka counseling aspects).