Large Language Models (LLMs) have shown promise in summarizing complex documents, but their limitations in handling lengthy documents and capturing global information hinder their performance in tasks like Query-Focused Summarization (QFS). We explore GraphRAG, a retrieval-augmented generation approach that utilizes a globally summarized knowledge graph derived from an LLM. We apply GraphRAG to the Financial Narrative Summarization (FNS) dataset, which consists of lengthy financial reports. Our results show that a naive RAG approach outperforms GraphRAG in terms of comprehensiveness, directness, conciseness and completeness. However, we demonstrate that optimizing entity and relation extraction using an LLM as an optimizer can enhance GraphRAG’s performance. Our study highlights the need for domain-specific optimization to improve GraphRAG’s capabilities for summarization tasks in facts-heavy domains like finance. We propose an optimization framework that extends GraphRAG’s original domain adaptation strategy by incorporating entity and relations optimization, leading to improved performance in capturing relevant entities and relationships. Our findings contribute to the development of more effective summarization models for complex documents in finance and other domains.