  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
counseling-component guided summarization model. ConSum undergoes three
independent modules. First, to assess the presence of depressive symptoms, it
filters utterances utilizing the Patient Health Questionnaire (PHQ-9), while
the second and third modules aim to classify counseling components. At last, we
propose a problem-specific Mental Health Information Capture (MHIC) evaluation
metric for counseling summaries. Our comparative study shows that we improve on
performance and generate cohesive, semantic, and coherent summaries. We
comprehensively analyze the generated summaries to investigate the capturing of
psychotherapy elements. Human and clinical evaluations on the summary show that
ConSum generates quality summary. Further, mental health experts validate the
clinical acceptability of the ConSum. Lastly, we discuss the uniqueness in
mental health counseling summarization in the real world and show evidences of
its deployment on an online application with the support of mpathic.ai
