Document summarization has long been a key task in natural language processing, with numerous methods proposed over the years. The advent of Transformer models has significantly improved performance for general-purpose summarization. However, specialized domains often require fine-tuning, which can be costly due to the need for curated training data. In this paper, we propose a document summarization framework that leverages named entity recognition (NER), focusing on cyber threat intelligence. We show that effective summarization is achievable using only publicly available data. Furthermore, we demonstrate that our proposed framework enables summarization incorporating generative AI such as GPT, and that it can generate effective summaries compared to simple zero-shot prompting summarization.