  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.
