Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering
Aseem Srivastava, Tharun Suresh, Sarah Peregrine (Grin) Lord, Md. Shad, Akhtar, Tanmoy Chakraborty

TL;DR
This paper introduces ConSum, a domain knowledge-guided summarization model for mental health counseling conversations, utilizing utterance filtering and classification to produce clinically relevant summaries validated by experts.
Contribution
It presents a new annotated dataset, a novel summarization model with domain-specific modules, and a specialized evaluation metric tailored for mental health counseling summaries.
Findings
ConSum outperforms baseline models in generating coherent summaries.
The model effectively filters and classifies counseling components.
Clinical evaluations confirm the summaries' quality and acceptability.
Abstract
The psychotherapy intervention technique is a multifaceted conversation between a therapist and a patient. Unlike general clinical discussions, psychotherapy's core components (viz. symptoms) are hard to distinguish, thus becoming a complex problem to summarize later. A structured counseling conversation may contain discussions about symptoms, history of mental health issues, or the discovery of the patient's behavior. It may also contain discussion filler words irrelevant to a clinical summary. We refer to these elements of structured psychotherapy as counseling components. In this paper, the aim is mental health counseling summarization to build upon domain knowledge and to help clinicians quickly glean meaning. We create a new dataset after annotating 12.9K utterances of counseling components and reference summaries for each dialogue. Further, we propose ConSum, a novel…
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Taxonomy
TopicsMental Health via Writing · Digital Mental Health Interventions · Topic Modeling
