In e-commerce, product detail pages (PDPs) often contain hundreds or thousands of customer reviews, yet the interface can surface only a small set of concise and reliable insights. A useful review aggregation system must therefore abstract recurring themes while preserving which reviews support each insight. We study this problem as structured review-insight mining: review-level tuples are first extracted and then aggregated into ranked product-level insight clusters with explicit member traceability. We propose GraphReduce, a coverage-preserving framework for product-scale review insight aggregation. It combines a contrastively trained projection layer over frozen sentence embeddings, graph-based clustering on local neighborhood structure, and an LLM reducer/ranker for naming and ordering canonical clusters. By keeping membership accounting outside the generative step, GraphReduce preserves complete tuple coverage and avoids a common failure mode of direct LLM summarization: producing plausible high-level themes while silently dropping long-tail evidence. Across three product-category benchmarks, GraphReduce improves recovery of gold insight clusters over strong lossless embedding-based baselines while maintaining comparable ranking quality. We further show that flat LLM and topic-modeling baselines can appear competitive under ranking metrics despite omitting substantial input evidence, and that an anti-copy reinforcement objective improves the quality of upstream tuple extraction. These results highlight the importance of coverage-aware aggregation for faithful review insight mining in e-commerce settings.