Synthesising Summaries: A novel Retrieval-Augmented Generation-based pipeline for multi-document summarisation
Abstract
The rapid growth of scientific literature in recent years has created a requirement for efficient methods to synthesise information from multiple related documents. This thesis addresses this challenge by developing and evaluating novel approaches to multi-document summarisation (MDS) of scientific papers, with a focus on hybrid and deep learning techniques leveraging both extractive and abstractive methods. The research explores the application of state-of-the-art large language models (LLMs), specifically Google's Gemma 2B and 7B models, to the task of scientific literature summarisation. A key innovative approach is the integration of Retrieval-Augmented Generation (RAG) techniques to enhance the summarisation process. The study employs a mixed-methods approach, combining quantitative evaluation metrics with qualitative human assessment and the recently developed novel LLM-as-judge methodology. A comprehensive literature review provides the theoretical foundation, covering the evolution of summarisation techniques, the emergence of transformer-based models, and recent advances in LLMs and related tools and techniques. The experimental design involves fine-tuning embedding models, optimising chunking strategies, and developing a RAG pipeline that integrates retrieval mechanisms with generative LLMs. Results demonstrate significant improvements in summary quality, coherence, and factual accuracy compared to baseline methods. The fine-tuned Gemma models, coupled with RAG…
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