Towards a Robust Retrieval-Based Summarization System
<|reference_start|>Towards a Robust Retrieval-Based Summarization System: 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.<|reference_end|>
@article{liu2024towards,
    title={Towards a Robust Retrieval-Based Summarization System},
    author={Shengjie Liu, Jing Wu, Jingyuan Bao, Wenyi Wang, Naira Hovakimyan,
  Christopher G Healey},
    journal={arXiv preprint arXiv:2403.19889},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19889},
    primaryClass={cs.CL cs.AI cs.IR cs.LG}
}