ConsistSum
Wenjun Ke, Jinhua Gao, Huawei Shen, Xueqi Cheng · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining · 2022
Unsupervised opinion summarization techniques are designed to condense the review data and summarize informative and salient opinions in the absence of golden references. Existing dominant methods generally follow a two-stage framework: first creating the synthetic "review-summary" paired datasets and then feeding them into the generative summary model for supervised training. However, these methods mainly focus on semantic similarity in synthetic dataset creation, ignoring the consistency of aspects and sentiments in synthetic pairs. Such inconsistency also brings a gap to the training and inference of the summarization model.