ConsistSum: Unsupervised Opinion Summarization with the Consistency of Aspect, Sentiment and Semantic

Wenjun Ke, Jinhua Gao, Huawei Shen, Xueqi Cheng

Abstract: 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. To alleviate this problem, we propose ConsistSum, an unsupervised opinion summarization method devoting to capture the consistency of aspects and sentiment between reviews and summaries. Specifically, ConsistSum first extracts the preliminary "review-summary" pairs from the raw corpus by evaluating the distance of aspect distribution and sentiment distribution. Then, we refine the preliminary summary with the constrained Metropolis-Hastings sampling to produce highly consistent synthetic datasets. In the summarization phase, we adopt the generative model T5 as the summarization model. T5 is fine-tuned for the opinion summarization task by incorporating the loss of predicting aspect and opinion distribution. Experimental results on two benchmark datasets, $i.e.$, Yelp and Amazon, demonstrate the superior performance of ConsistSum over the state-of-the-art baselines.