QAugment-Summary: Multi-stage Summarization and Question Decomposition for Open-Domain QA

Large Language Models (LLMs) have demonstrated strong generative capabilities in open-domain question answering (ODQA), but the lagging nature of their parameterized knowledge limits their ability to handle time-sensitive queries. Although Retrieval-Augmented Generation (RAG) mitigates this issue by introducing external knowledge, existing approaches primarily focus on optimizing retrieved passages, and their performance remains highly dependent on the quality of initial retrieval. To address this limitation, we propose the QAugment-Summary framework, which aims to enhance LLM performance in ODQA tasks through fine-grained evidence collection and deep integration. The framework first performs question augmentation and sub-question decomposition to enable more targeted information retrieval. Then, it adopts a collaborative retrieval-generation approach to collect candidate evidence for each sub-question. The core innovation lies in our multi-stage summarization mechanism: the system independently summarizes both the retrieved passages and the complementary passages generated by the LLM, then concatenates these summaries and conducts a second-stage deep summarization to form a highly condensed and information-rich explicit reasoning basis. This final summary effectively handles long-text context and filters irrelevant information, while also serving as a robust foundation for LLMs to generate the final answer. Experimental results on multiple ODQA benchmark datasets demonstrate a 9.5-point gain in Exact Match (EM) scores, indicating that QAugment-Summary substantially improves answer accuracy, information integration, and cross-document reasoning, thereby offering a promising direction for building more reliable LLM-based reasoning systems.