Unsupervised Opinion Summarization Using Approximate Geodesics
This addresses the problem of summarizing user reviews for applications like e-commerce or recommendation systems, representing an incremental improvement with a novel scoring mechanism.
The paper tackles unsupervised extractive opinion summarization by introducing Geodesic Summarizer (GeoSumm), which uses dictionary learning and approximate geodesic distances to identify popular opinions from reviews, achieving state-of-the-art performance on three datasets.
Opinion summarization is the task of creating summaries capturing popular opinions from user reviews. In this paper, we introduce Geodesic Summarizer (GeoSumm), a novel system to perform unsupervised extractive opinion summarization. GeoSumm involves an encoder-decoder based representation learning model, that generates representations of text as a distribution over latent semantic units. GeoSumm generates these representations by performing dictionary learning over pre-trained text representations at multiple decoder layers. We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism. We use the relevance scores to identify popular opinions in order to compose general and aspect-specific summaries. Our proposed model, GeoSumm, achieves state-of-the-art performance on three opinion summarization datasets. We perform additional experiments to analyze the functioning of our model and showcase the generalization ability of {\X} across different domains.