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Proposed architecture for product review summarisation: a modular pipeline for aspect-based insight extraction and synthesis
(КПІ ім. Ігоря Сікорського, 2025) Panasiuk, Yaroslav
In the domain of e-commerce, user reviews are a critical resource for decision-making. However, the sheer volume and unstructured nature of these reviews present a significant challenge for consumers seeking actionable insights. Traditional extractive summarization methods often fail to capture nuance, while end-to-end Large Language Model (LLM) approaches struggle with hallucinations and lack of structural control. This paper proposes a novel, linear, multi-stage architecture that transforms unstructured text into the Quantified Aspect-Based Summary (QABS). The proposed pipeline utilizes a modular approach, integrating Coreference Resolution, Low-Rank Adaptation (LoRA) finetuned models for tuple extraction, and dynamic topic modeling. By decomposing reviews into atomic insights and re-synthesizing them using blueprint prompting, this architecture ensures high clarity, trust, verifiability, and quantification of user sentiment.