In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

中文翻译：

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在线系统中的审查摘要生成：有监督和无监督场景的框架

在包括电商平台在内的在线系统中，很多用户会求助于之前消费者产生的评论或评论进行决策，而他们的时间有限，无法处理很多评论。因此，需要一个包含用户生成评论中所有重要特征的评论摘要。在本文中，我们研究“如何从大量用户生成的评论中生成全面的评论摘要”。这可以通过文本摘要来实现，文本摘要主要有提取和抽象两种方法。这两种方法都可以处理有监督和无监督的场景，但前者可能会产生冗余和不连贯的摘要，而后者可以避免冗余但通常只能处理短序列。此外，这两种方法都可能忽略情感信息。审查摘要生成处理有监督和无监督场景的框架。我们设计了两种不同的重新排序和选择预处理模型，以识别重要句子，同时保持用户在原始评论中的情绪。这些句子可以进一步用于生成带有文本摘要方法的评论摘要。七个真实世界数据集（Idebate、Rotten Tomatoes Amazon、Yelp 和亚马逊中的三个未标记产品评论数据集）的实验结果表明，我们的工作在评论摘要生成方面表现良好。此外，重新排序和选择模型表现出不同的特征。