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    <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 consists of an encoder-decoder based representation learning model that generates topical representations of texts. These representations capture the underlying semantics of the text as a distribution over learnable latent units. GeoSumm generates these topical representations by performing dictionary learning over pre-trained text representations at multiple layers of the decoder. We then use these topical representations to quantify the importance of review sentences using a novel approximate geodesic distance-based scoring mechanism. We use the importance scores to identify popular opinions in order to compose general and aspect-specific summaries. Our proposed model, GeoSumm, achieves strong performance on three opinion summarization datasets. We perform additional experiments to analyze the functioning of our model and showcase the generalization ability of GeoSumm across different domains.</abstract>
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%0 Conference Proceedings
%T Unsupervised Opinion Summarization Using Approximate Geodesics
%A Basu Roy Chowdhury, Somnath
%A Monath, Nicholas
%A Dubey, Kumar
%A Ahmed, Amr
%A Chaturvedi, Snigdha
%Y Dong, Yue
%Y Xiao, Wen
%Y Wang, Lu
%Y Liu, Fei
%Y Carenini, Giuseppe
%S Proceedings of the 4th New Frontiers in Summarization Workshop
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F basu-roy-chowdhury-etal-2023-unsupervised
%X 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 consists of an encoder-decoder based representation learning model that generates topical representations of texts. These representations capture the underlying semantics of the text as a distribution over learnable latent units. GeoSumm generates these topical representations by performing dictionary learning over pre-trained text representations at multiple layers of the decoder. We then use these topical representations to quantify the importance of review sentences using a novel approximate geodesic distance-based scoring mechanism. We use the importance scores to identify popular opinions in order to compose general and aspect-specific summaries. Our proposed model, GeoSumm, achieves strong performance on three opinion summarization datasets. We perform additional experiments to analyze the functioning of our model and showcase the generalization ability of GeoSumm across different domains.
%R 10.18653/v1/2023.newsum-1.11
%U https://aclanthology.org/2023.newsum-1.11
%U https://doi.org/10.18653/v1/2023.newsum-1.11
%P 105-120

Markdown (Informal)

[Unsupervised Opinion Summarization Using Approximate Geodesics](https://aclanthology.org/2023.newsum-1.11) (Basu Roy Chowdhury et al., NewSum 2023)

• Unsupervised Opinion Summarization Using Approximate Geodesics (Basu Roy Chowdhury et al., NewSum 2023)

ACL

• Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Dubey, Amr Ahmed, and Snigdha Chaturvedi. 2023. Unsupervised Opinion Summarization Using Approximate Geodesics. In Proceedings of the 4th New Frontiers in Summarization Workshop, pages 105–120, Singapore. Association for Computational Linguistics.