reviews: 
Comments: This paper presents a tool designed to help mental health therapists by summarizing counseling sessions into mind maps. Using a lightweight language mode GPT-4o minil, the tool takes session transcripts and generates visual summaries that highlight key points in a structured, easy-to-read format. This approach aims to reduce the cognitive load on therapists by providing quick, organized overviews of each session, making it easier to recall important details. The study also includes human evaluations, showing that these visual summaries effectively capture the main elements of a session, potentially enhancing therapeutic practice by simplifying documentation.
Strengths: Originality:
The paper introduces the use of mind maps for summarizing counseling transcripts, which is an interesting shift from typical text summaries. Applying language models to generate these visuals in a mental health context is a practical adaptation, though it builds on existing summarization methods.
Quality:
The method is clearly explained but lacks depth in certain areas. While the pipeline from prompt design to mind map visualization is understandable, the approach could benefit from a stronger methodological foundation or more comprehensive testing. Human evaluations are included, but the evaluation setup could be more rigorous to fully support the tool’s effectiveness.
Clarity:
The paper explains the problem and solution in a straightforward way, with enough detail to understand the main approach. Visual examples help clarify how the mind maps work, although some sections could be streamlined for readability.
Significance:
Addressing the cognitive load for therapists is a relevant issue, and this tool could be helpful in simplifying their workflow. While the impact is practical, the approach is incremental and may inspire further exploration of visual summarization but is unlikely to be transformative on its own.
Weaknesses: 1. Limited Sample Size in Evaluation
Although the authors mention that they selected 20 transcripts as a “preliminary” sample, the decision to limit the entire study to these 20 samples is problematic given that the original MEMO dataset contains 212 transcripts. Relying solely on such a small subset, when a much larger set is available, raises concerns about the representativeness of the findings. A larger, more diverse sample could provide a stronger basis for evaluating the tool’s reliability across different counseling scenarios and patient-therapist interactions. Expanding the sample size is essential to enhancing the credibility of the results and giving a fuller picture of the tool's performance.
2. Insufficient Evaluation Criteria and Sample Size of Evaluators
The evaluation approach has two issues: (1) the limited expertise of the evaluators and (2) the small number of evaluators. Referring to the evaluators as “participants” implies they were not specially trained or highly qualified to evaluate counseling summaries, which could compromise the quality of feedback. A robust evaluation for this type of tool typically requires input from domain experts — such as mental health professionals — who can reliably assess criteria like accuracy, relevance, and therapeutic usefulness based on their experience.
Additionally, the study relies on only three evaluators, which is insufficient for a reliable, statistically meaningful assessment. This small evaluator pool, combined with limited experience in mental health, limits the confidence one can have in the evaluation results.
3. Lack of Comparisons with Other Summarization Methods and Models
The paper does not compare mind maps with alternative summarization formats, such as text-based summaries or visual formats like concept maps. This absence weakens the justification for mind maps as the preferred format, as their claimed cognitive benefits remain untested. Additionally, the study only uses GPT-4o Mini, without comparing it to other language models. This limits the scope since other models might perform better or capture nuances differently.

By addressing these issues — expanding the sample size, involving a more qualified evaluator pool, and incorporating comparisons with other methods — the study would achieve a higher standard of rigor and offer stronger evidence for the proposed tool’s effectiveness.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2