R evie W eaver: Weaving Together Review Insights by Leveraging LLM s and Semantic Similarity (2025)

Abstract: With the rise of online retail, customer reviews have become a critical factor in shaping purchasing decisions. The sheer volume of customer reviews being generated continuously presents a challenge for consumers who must sift through an overwhelming amount of feedback. To address this issue, we introduce RevieWeaver, a novel framework that extracts key product features and provides concise review summaries. Our innovative approach not only scales efficiently to 30 million reviews but also ensures reproducibility and controllability. Moreover, it delivers unbiased and reliable assessments of products that accurately reflect the input reviews.

## introduction at best buy foot_0 , a substantial number of customer reviews are collected daily, resulting in a comprehensive collection of shared experiences for each product. over time, these reviews can accumulate to tens of thousands, providing an opportunity to uncover valuable insights into the product's strengths and weaknesses. research shows that customer reviews significantly influence purchasing decisions @xcite . during the shopping experience, customers can examine a set of reviews left by previous customers, allowing them to gain a deeper understanding of the product's features and drawbacks. however, when a product has an excessive amount of reviews, this process can become overwhelming. providing a condensed list of a product's key features, pros, and cons, along with a brief summary of customer opinions can help mitigate this issue. this approach enables customers to quickly and efficiently assess the product's strengths and weaknesses, without being bogged down by an excessive amount of information. ## problem statement let @xmath0, r 2 , ..., r n } be a set of customer reviews for a product p , where each review r i is a sequence of words. we have mainly two tasks: ## approach we propose a unified framework named re-vieweaver to extract high-level product features from customer reviews and generate a concise and helpful summary of the reviews. ## deployment at best buy, we utilize google cloud to host our data analytics and machine learning operations. figure 3 shows the deployment pipelines used to run revieweaver. to execute the proposed framework, we package revieweaver as a python package to be executed on multiple cloud instances, as illustrated in the top section. we then leverage a series of google cloud services to schedule and trigger pipelines, which employ the built package to process the reviews and produce the final output for customer display. this strategic approach enables us to decouple our code, deployment, and hardware, allowing us to utilize the same infrastructure for both experimental and large-scale production runs. to date, our framework has successfully processed approximately 30 million reviews across a staggering 200,000 prod-uct categories, demonstrating its robustness and scalability. ## discussion one of our focuses in this work was to ensure that the produced data could be extracted effectively at scale, and to ensure that we produce fair and controllable review distillation and summaries. scalability was achieved by decoupling the aspect extraction. when the llm is used, the data is cached for later use. the grouping and ranking steps can be run multiple times without the need to re-run the costly aspect extraction step. for new incoming reviews, continuous updates will also cost less since new reviews will be processed once. as a result, the long-term costs for aspect extraction will be capped. for the review summarization process, we effectively reduced the number of input tokens and, consequently, the associated cost for summary generation using the llm. since we use at most the top 10 positive features, the top 10 negative features, and the top 10 representative sentences for each feature, the upper limit of context size will always be capped at a certain number of tokens irrespective of the total number of reviews. this significantly reduced the cost of summarizing the content of products that have thousands of reviews. one limitation of our work is that we only used a single model to evaluate the summaries, primarily due to enterprise policies and privacy concerns. however, we believe that using multiple models would have yielded similar judgments. controllability is crucial in industrial settings, since such systems are semi-autonomous and we cannot manually review each output. we have seen that our approach produces repeatable outputs across diverse product categories. lastly, as a retailer, its our responsibility to surface unbiased and fair information to the customer, and let them use it to aid their purchasing decision. using revieweaver, we ensured that both pros and cons are adequately represented in both review distillation and product summaries. ## conclusion we have shown that revieweaver addresses some of the main challenges in review distillation and summarization. in our experiments and realworld application, we saw that revieweaver outperforms other methods both in empirical metrics and in reproducibility, cost effectiveness, and fairness. austin, texas. association for computational linguistics. xinyi wang, wanrong zhu, michael saxon, mark steyvers, and william yang wang. 2024. large language models are latent variable models: explaining and finding good demonstrations for in-context learning. in proceedings of the 37th international conference on neural information processing systems, nips '23, red hook, ny, usa. curran associates inc. haiyan wu, chaogeng huang, and shengchun deng. 2023. improving aspect-based sentiment analysis with knowledge-aware dependency graph network. information fusion, 92:289-299. heng yang, chen zhang, and ke li. 2023. pyabsa: a modularized framework for reproducible aspect-based sentiment analysis. in proceedings of the 32nd acm international conference on information and knowledge management, cikm 2023, birmingham, united kingdom, october 21-25, 2023, pages 5117-5122. acm.