An unsupervised opinion summarization model fused joint attention and dictionary learning

Unsupervised opinion summarization is the technique of automatically generates summaries without gold reference, and the summaries that reflects aspects of information about the entity. Although there are more mature studies on unsupervised opinion summarazaiton, but these studies focus more on unsupervised training methods and ignore the extraction of information by the model. In this paper, we propose JointSum, an unsupervised opinion summarization method based on variational autoencoder model. JointSum first extracts aspect and sentiment information in reviews by joint attention and dictionary learning, respectively. Joint attention consists of text attention and auxiliary attention, which can extract key information in the input text from different fine-grained levels. Then we calculate the variance and mean of the Gaussian distribution in variational autoencoder model using aspect and sentiment information. In addition, we added the review score prediction subtask to increase the robustness of the model. Finally, in generation phase, we adopt pointer-generator network because it includes copy and coverage mechanism that can solve problems in text generation. Experiments on Amazon and Yelp datasets, the results show that the model has good performance in both automatic and human evaluation, the ROUGE-L value on the Yelp dataset gets 20.83.