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.

翻译：抽取式观点摘要旨在通过从评论文本（如产品评论）中抽取代表性句子，自动生成关于某个实体（例如产品）的摘要，以捕获评论集中普遍存在的观点。在在线市场中，用户评论通常会随时间累积，观点摘要需要定期更新，以便为客户提供最新信息。本研究探讨了在增量场景下的抽取式观点摘要任务，其中底层的评论集随时间演变。许多最先进的抽取式观点摘要方法都是基于中心性（centrality-based）的，例如CentroidRank。CentroidRank通过选择表示空间中距离质心最近的评论句子子集作为摘要，执行抽取式摘要。然而，这些方法无法在评论逐一到达的增量场景下高效运行。本文提出了一种高效算法，用于在增量场景下精确计算CentroidRank摘要。我们的方法CoverSumm依赖于将评论表示索引到覆盖树（cover tree）中，并维护一个候选摘要评论句子的储备池（reservoir）。CoverSumm的有效性得到了运行时间的理论和实证分析支持。在多种数据集（包括真实数据和为展示扩展性考虑而人工创建的数据）上的实验表明，CoverSumm比基线方法快高达25倍，并且能够适应数据分布的细微变化。我们还对生成的摘要进行了人工评估，发现CoverSumm能够生成与底层评论集一致且信息丰富的摘要。