Towards a Robust Retrieval-Based Summarization System

• URL: http://arxiv.org/abs/2403.19889v1
• Date: Fri, 29 Mar 2024 00:14:46 GMT
• Title: Towards a Robust Retrieval-Based Summarization System
• Authors: Shengjie Liu, Jing Wu, Jingyuan Bao, Wenyi Wang, Naira Hovakimyan, Christopher G Healey,
• Abstract summary: This paper describes an investigation of the robustness of large language models (LLMs) for retrieval augmented generation (RAG)-based summarization tasks. Our first contribution is LogicSumm, an innovative evaluation framework incorporating realistic scenarios. 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.
• Score: 11.747998334533776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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