  Multi-hop question answering is a challenging task with distinct industrial
relevance, and Retrieval-Augmented Generation (RAG) methods based on large
language models (LLMs) have become a popular approach to tackle this task.
Owing to the potential inability to retrieve all necessary information in a
single iteration, a series of iterative RAG methods has been recently
developed, showing significant performance improvements. However, existing
methods still face two critical challenges: context overload resulting from
multiple rounds of retrieval, and over-planning and repetitive planning due to
the lack of a recorded retrieval trajectory. In this paper, we propose a novel
iterative RAG method called ReSP, equipped with a dual-function summarizer.
This summarizer compresses information from retrieved documents, targeting both
the overarching question and the current sub-question concurrently.
Experimental results on the multi-hop question-answering datasets HotpotQA and
2WikiMultihopQA demonstrate that our method significantly outperforms the
state-of-the-art, and exhibits excellent robustness concerning context length.
