这篇论文找到了一个用LLM做观点摘要的新路子，能省下不少token，还能保留不同人的真实观点，比常规摘要方法更靠谱。
该论文提出一种用大语言模型进行观点摘要的框架，结合多维分类（如情感、主题）和分层采样策略，先在Amazon产品评论、Tripadvisor酒店评论和X/Twitter帖子等数据集上筛选出紧凑且有代表性的意见子集，再通过定制提示生成平衡摘要。实验表明，该方法在保持语义的同时显著降低了Token使用量和计算成本，在内容覆盖、平衡性和语义保留上一致优于传统AI和标准LLM摘要基线。
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 the LLM. Tailored prompts then produce balanced summaries that surface the salient aspects expressed in the opinions (e.g., strengths and weaknesses of products/hotels). Experiments on Amazon product reviews, Tripadvisor hotel reviews, and X/Twitter posts demonstrate that our method significantly reduces token usage and computational cost while consistently outperforming traditional AI-based and standard LLM summarization baselines in terms of content coverage, balance, and semantic preservation.