Unsupervised Opinion Summarization Using Approximate Geodesics

• URL: http://arxiv.org/abs/2209.07496v3
• Date: Mon, 20 Nov 2023 15:57:43 GMT
• Title: Unsupervised Opinion Summarization Using Approximate Geodesics
• Authors: Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
• Abstract summary: 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. We use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism.
• Score: 38.19696482602788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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