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 accrue 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.
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 25x 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.

本文研究了在增量设置中进行提取式意见摘要的任务，提出了一种基于 CoverSumm 的高效算法来准确计算 CentroidRank 摘要，并通过理论和实验分析验证了其有效性。实验结果表明，CoverSumm 方法比基线方法快 25 倍，并且能够适应数据分布的细微变化。