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Towards a Robust Retrieval-Based Summarization System (2403.19889v1)

Published 29 Mar 2024 in cs.CL, cs.AI, cs.IR, and cs.LG

Abstract: This paper describes an investigation of the robustness of 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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Towards a Robust Retrieval-Based Summarization System

Introduction

In the domain of automated text summarization, leveraging the capabilities of LLMs augmented with Retrieval Augmented Generation (RAG) techniques introduces a promising avenue for generating accurate, coherent summaries of complex content. The integration of RAG enables these models to dynamically incorporate fresh information from external sources into the text generation process, potentially addressing the problem of outdated or incomplete knowledge bases inherent in statically trained LLMs. However, the robustness of LLMs in RAG-based summarization, particularly their performance across various realistic scenarios, remains an area ripe for exploration. This paper introduces LogicSumm, an evaluation framework designed to assess the summarization prowess of LLMs within RAG-fortified environments across a suite of common summarization scenarios. Alongside, the development of SummRAG, a comprehensive system aiming to refine the robustness of LLMs through dialogue generation and model fine-tuning, is discussed. This system, an embodiment of structured problem-solving over ad-hoc adjustments, showcases improved performance in logical coherence and summarization quality.

LogicSumm: A Novel Evaluation Framework

The core of the paper lies in the introduction of LogicSumm, an evaluation framework explicitly crafted for RAG-based summarization tasks employing LLMs. LogicSumm dissects the summarization process into seven distinct scenarios, each designed to encapsulate a common challenge encountered in real-world summarization tasks. These scenarios are meticulously structured to assess an LLM's competency in recognizing document relevance, summarizing from both provided and retrieved texts, and integrating multiple documents into a coherent summary. The framework's rigor in evaluating the nuanced abilities of LLMs to discern relevance, manage information conflicts, and dynamically adapt to the varied requirements of each summarization task is pivotal in quantifying the robustness of these models.

SummRAG: Advancing Model Robustness

The identification of limitations within the current capabilities of LLMs, as surfaced by LogicSumm, has catalyzed the development of SummRAG. This system ventures beyond the traditional model training paradigms by generating dialogues contextualized to the summarization scenarios outlined in LogicSumm, hence allowing for targeted fine-tuning of models. The integration of special tokens and the application of novel dialogue generation strategies, facilitated by GPT-4 Turbo, sets the foundation for SummRAG's model fine-tuning process. Notably, SummRAG’s comprehensive approach, encompassing the generation of scenario-specific dialogues to direct model tuning, embodies a strategic advancement towards refining the summarization capabilities of LLMs under complex scenarios.

Empirical Insights and Theoretical Contributions

The empirical evaluation of SummRAG, grounded in the LogicSumm framework, underscores the system's efficacy in bolstering the logical accuracy and summarization quality of LLMs. Competing models, subjected to the same scenarios, delineate the comparative advantages conferred by SummRAG's methodology. The fine-tuning process, informed by the nuanced requirements identified through LogicSumm, enables the model to achieve noteworthy improvements in handling information relevance, conflict, and integration across multiple documents, corroborating the system's theoretical underpinnings.

Implications and Future Directions

The research encapsulated in this paper provides a two-fold contribution to the field of AI and text summarization. Firstly, it enhances the understanding of LLMs' performance spectra within RAG-based summarization tasks, offering a granular view of existing capabilities and deficits. Secondly, the development and implementation of SummRAG present a methodological framework for increasing the robustness and accuracy of these models in a structured manner. Looking ahead, the paper lays fertile ground for future explorations into developing more encompassing evaluation frameworks and refining LLM training methodologies to further elevate their performance in real-world summarization tasks.

Conclusion

This paper introduces LogicSumm and SummRAG as pioneering efforts to probe and enhance the robustness of LLMs within the field of RAG-based summarization. Through a meticulous evaluation and fine-tuning process, the research underscores significant strides towards realizing the full potential of LLMs in generating coherent, accurate summaries across a spectrum of complex scenarios. The findings beckon further inquiry into comprehensive evaluation frameworks and advanced training methodologies, heralding a promising avenue for future advancements in LLM-based text summarization.

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Authors (6)
  1. Shengjie Liu (17 papers)
  2. Jing Wu (182 papers)
  3. Jingyuan Bao (1 paper)
  4. Wenyi Wang (35 papers)
  5. Naira Hovakimyan (114 papers)
  6. Christopher G Healey (1 paper)
Citations (4)
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