  Nursing notes, an important component of Electronic Health Records (EHRs),
keep track of the progression of a patient's health status during a care
episode. Distilling the key information in nursing notes through text
summarization techniques can improve clinicians' efficiency in understanding
patients' conditions when reviewing nursing notes. However, existing
abstractive summarization methods in the clinical setting have often overlooked
nursing notes and require the creation of reference summaries for supervision
signals, which is time-consuming. In this work, we introduce QGSumm, a
query-guided self-supervised domain adaptation framework for nursing note
summarization. Using patient-related clinical queries as guidance, our approach
generates high-quality, patient-centered summaries without relying on reference
summaries for training. Through automatic and manual evaluation by an expert
clinician, we demonstrate the strengths of our approach compared to the
state-of-the-art Large Language Models (LLMs) in both zero-shot and few-shot
settings. Ultimately, our approach provides a new perspective on conditional
text summarization, tailored to the specific interests of clinical personnel.
