  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 operating efficiently in an incremental setting, where reviews
arrive one at a time. In this paper, we present an efficient algorithm for
accurately computing the CentroidRank summaries in an incremental setting. Our
approach, CoverSumm, relies on indexing review representations in a cover tree
and maintaining a reservoir of candidate summary review sentences. CoverSumm's
efficacy is supported by a theoretical and empirical analysis of running time.
Empirically, on a diverse collection of data (both real and synthetically
created to illustrate scaling considerations), we demonstrate that CoverSumm is
up to 36x faster than baseline methods, and capable of adapting to nuanced
changes in data distribution. We also conduct human evaluations of the
generated summaries and find that CoverSumm is capable of producing informative
summaries consistent with the underlying review set.
