  This paper describes an investigation of the robustness of large language
models (LLMs) for retrieval augmented generation (RAG)-based summarization
tasks. While LLMs provide summarization capabilities, their performance in
complex, real-world scenarios remains under-explored. Our first contribution is
LogicSumm, an innovative evaluation framework incorporating realistic scenarios
to assess LLM robustness during RAG-based summarization. Based on limitations
identified by LogiSumm, we then developed SummRAG, a comprehensive system to
create training dialogues and fine-tune a model to enhance robustness within
LogicSumm's scenarios. SummRAG is an example of our goal of defining structured
methods to test the capabilities of an LLM, rather than addressing issues in a
one-off fashion. Experimental results confirm the power of SummRAG, showcasing
improved logical coherence and summarization quality. Data, corresponding model
weights, and Python code are available online.
