Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization
Opinionated text - spanning product reviews, hotel feedback, and social posts - captures rich signals about user experiences, preferences, and concerns. However, the scale, redundancy, and imbalance of such corpora make it challenging to analyze opinions effectively, particularly when the goal is to generate summaries that remain faithful to the diversity of viewpoints expressed. This paper presents a framework that preserves semantics in LLM-based opinion summarization while minimizing token usage. We combine multidimensional classification (e.g., sentiment, topics) with a family of stratified sampling strategies to select compact yet representative subsets of opinions before prompting t...
| Authors | Fabrizio Marozzo, Stefano Iannicelli |
|---|---|
| Primary category | cs.CL |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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