Contact centers are critical for delivering high-quality customer service to various businesses. Call summarization is a crucial task for contact center agents for compliance, to transfer contextual information to the next agent, or to serve as a reference for future interactions. Agents spend a substantial amount of time writing notes on or after a call, which reduces their productivity and adds to the cost per call. While there exist various pre-trained Large Language Models (LLM) for summarization, they often lack coverage of domain-specific information relevant to businesses. We propose a hybrid streaming notes generation system leveraging the generative capabilities of an LLM fine-tuned for contact center call summarization, but allowing businesses to focus notes generation around events of business interest. Our system reduces after-call work for agents by not only generating notes out-of-the-box but also allowing agents to edit them in real time due to its streaming nature.