Distilling Opinions at Scale: Incremental Opinion Summarization using XL-OPSUMM
This addresses the scalability issue in opinion summarization for e-commerce platforms, but it is incremental as it builds on existing methods with a new dataset.
The paper tackles the problem of summarizing thousands of short e-commerce reviews, which exceeds LLM context limits, by proposing an incremental framework called Xl-OpSumm, achieving average ROUGE-1 and ROUGE-L F1 gains of 4.38% and 3.70% over the next best model.
Opinion summarization in e-commerce encapsulates the collective views of numerous users about a product based on their reviews. Typically, a product on an e-commerce platform has thousands of reviews, each review comprising around 10-15 words. While Large Language Models (LLMs) have shown proficiency in summarization tasks, they struggle to handle such a large volume of reviews due to context limitations. To mitigate, we propose a scalable framework called Xl-OpSumm that generates summaries incrementally. However, the existing test set, AMASUM has only 560 reviews per product on average. Due to the lack of a test set with thousands of reviews, we created a new test set called Xl-Flipkart by gathering data from the Flipkart website and generating summaries using GPT-4. Through various automatic evaluations and extensive analysis, we evaluated the framework's efficiency on two datasets, AMASUM and Xl-Flipkart. Experimental results show that our framework, Xl-OpSumm powered by Llama-3-8B-8k, achieves an average ROUGE-1 F1 gain of 4.38% and a ROUGE-L F1 gain of 3.70% over the next best-performing model.