Token-Efficient LLM Framework for Opinion Summarization Preserves Semantics

Jul 14, 2026

A new framework combines multidimensional classification and stratified sampling to select representative subsets of opinions before summarization by large language models (LLMs). This approach reduces token usage and computational cost, while experiments on product reviews, hotel feedback, and social posts show improved coverage, balance, and semantic preservation compared to traditional and standard LLM summarization baselines.

Why it matters: Efficiently summarizing large-scale opinionated text without sacrificing viewpoint diversity is crucial for applications such as product analysis and social listening.

Full story at: arXiv Computation and Language ↗