Unsupervised Opinion Summarisation in the Wasserstein Space

Abstract

Opinion summarisation synthesises opinions expressed in a group of documents discussingthe same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the preservation of meaning compared to online reviews, which has been so far the focus on opinion summarisation. To address these challenges we present WassOS, an unsupervised abstractive summarization model which makesuse of the Wasserstein distance. A Variational Autoencoder is first used to obtain the distribution of documents/posts, and the summary distribution is obtained as the Wasserstein barycenter. We create separate disentangled latent semantic and syntactic representations of the summary, which are fed into a GRU decoder with a transformer layer to produce the final summary. Our experiments onmultiple datasets including reviews, Twitter clusters and Reddit threads show that WassOSalmost always outperforms the state-of-the-art on ROUGE metrics and consistently producesthe best summaries with respect to meaning preservation according to human evaluations.