Incremental Extractive Opinion Summarization Using Cover Trees
Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Manzil, Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi

TL;DR
This paper introduces CoverSumm, an efficient incremental extractive opinion summarization method using cover trees, enabling fast updates and maintaining summary quality as review data evolves over time.
Contribution
We propose CoverSumm, a novel algorithm that efficiently updates opinion summaries incrementally using cover trees, outperforming existing methods in speed and adaptability.
Findings
CoverSumm is up to 36x faster than baseline methods.
It effectively adapts to changes in review data distribution.
Human evaluations show summaries are informative and consistent.
Abstract
Extractive opinion summarization involves automatically producing a summary of text about an entity (e.g., a product's reviews) by extracting representative sentences that capture prevalent opinions in the review set. Typically, in online marketplaces user reviews accumulate over time, and opinion summaries need to be updated periodically to provide customers with up-to-date information. In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time. Many of the state-of-the-art extractive opinion summarization approaches are centrality-based, such as CentroidRank (Radev et al., 2004; Chowdhury et al., 2022). CentroidRank performs extractive summarization by selecting a subset of review sentences closest to the centroid in the representation space as the summary. However, these methods are not capable of…
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Taxonomy
TopicsTopic Modeling · Advanced Text Analysis Techniques · Natural Language Processing Techniques
MethodsSparse Evolutionary Training
