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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T GraphReduce: Coverage-Preserving LLM Aggregation for E-commerce Review Insights
%A Jiang, Hao
%A Li, Xin
%A Zhang, Yichi
%A Lin, Weisi
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F jiang-etal-2026-graphreduce
%X 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.
%U https://aclanthology.org/2026.emnlp-industry.91/
%P 1545-1557

Markdown (Informal)

[GraphReduce: Coverage-Preserving LLM Aggregation for E-commerce Review Insights](https://aclanthology.org/2026.emnlp-industry.91/) (Jiang et al., EMNLP 2026)

• GraphReduce: Coverage-Preserving LLM Aggregation for E-commerce Review Insights (Jiang et al., EMNLP 2026)

ACL

• Hao Jiang, Xin Li, Yichi Zhang, and Weisi Lin. 2026. GraphReduce: Coverage-Preserving LLM Aggregation for E-commerce Review Insights. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1545–1557, Budapest, Hungary. Association for Computational Linguistics.