As more than 70$\%$ of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are reluctant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the over-reliance on a specific framework is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, data augmentation based on large language models faces two disadvantages: 1) the potential issues or toxicity in the augmented data; 2) the expensive costs. Therefore, in this paper, we propose a novel data augmentation framework based on both large and small language models for debiasing opinion summarization. In specific, a small size of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on confusion degree and sentiment classification. Experiments have proved that our framework can effectively alleviate emotional bias same as using only large models, but more economically.

翻译：现有意见摘要数据集中超过70%的评论为正面评价，导致当前意见摘要方法在输入负面文本时倾向于不生成负面摘要。为处理这种情感偏差，一种不依赖特定框架的直接方法是通过大语言模型生成额外数据来平衡数据集的情感分布。然而，基于大语言模型的数据增强面临两个缺陷：1）增强数据中可能存在的有害或毒性内容；2）高昂的成本。因此，本文提出一种同时基于大语言模型和小语言模型的新型数据增强框架用于去偏意见摘要。具体而言，首先通过大语言模型将正面文本改写为少量合成负面评论文本。随后基于生成数据训练解耦重构模型。训练完成后，通过融合不同样本表示得到的新表征进行解码，并基于困惑度和情感分类进行过滤，即可获得大量合成数据。实验证明，本框架能像仅使用大模型一样有效缓解情感偏差，但成本更为经济。