When faced with a large number of product reviews, it is not clear that a
human can remember all of them and weight opinions representatively to write a
good reference summary. We propose an automatic metric to test the prevalence
of the opinions that a summary expresses, based on counting the number of
reviews that are consistent with each statement in the summary, while
discrediting trivial or redundant statements. To formulate this opinion
prevalence metric, we consider several existing methods to score the factual
consistency of a summary statement with respect to each individual source
review. On a corpus of Amazon product reviews, we gather multiple human
judgments of the opinion consistency, to determine which automatic metric best
expresses consistency in product reviews. Using the resulting opinion
prevalence metric, we show that a human authored summary has only slightly
better opinion prevalence than randomly selected extracts from the source
reviews, and previous extractive and abstractive unsupervised opinion
summarization methods perform worse than humans. We demonstrate room for
improvement with a greedy construction of extractive summaries with twice the
opinion prevalence achieved by humans. Finally, we show that preprocessing
source reviews by simplification can raise the opinion prevalence achieved by
existing abstractive opinion summarization systems to the level of human
performance.

面对大量的产品评论，我们提出了一种自动度量标准来测试摘要表达的观点普遍性，基于统计与摘要中每个陈述一致的评论数量，同时贬低琐碎或冗余的陈述。通过使用所得到的观点普遍性度量标准，我们展示了人工撰写的摘要仅比随机选取的源评论摘录稍微具有更好的观点普遍性，并且之前的抽取式和抽象式无监督观点摘要方法比人工表现更差。我们展示了通过贪婪构建抽取摘要可以改进，达到了两倍于人工的观点普遍性。最后，我们展示了通过简化源评论的预处理可将现有的抽象观点摘要系统的观点普遍性提高到人工表现水平。

自动评估观点总结中的观点普遍性

Automatically Evaluating Opinion Prevalence in Opinion Summarization

We propose a method for unsupervised opinion summarization that encodes
sentences from customer reviews into a hierarchical discrete latent space, then
identifies common opinions based on the frequency of their encodings. We are
able to generate both abstractive summaries by decoding these frequent
encodings, and extractive summaries by selecting the sentences assigned to the
same frequent encodings. Our method is attributable, because the model
identifies sentences used to generate the summary as part of the summarization
process. It scales easily to many hundreds of input reviews, because
aggregation is performed in the latent space rather than over long sequences of
tokens. We also demonstrate that our appraoch enables a degree of control,
generating aspect-specific summaries by restricting the model to parts of the
encoding space that correspond to desired aspects (e.g., location or food).
Automatic and human evaluation on two datasets from different domains
demonstrates that our method generates summaries that are more informative than
prior work and better grounded in the input reviews.

我们提出了一种无监督的意见摘要方法，该方法将顾客评论中的句子编码到分层离散的潜空间中，然后基于编码的频率识别常见的意见，并通过解码这些频繁的编码生成抽象摘要和通过选择分配给相同频繁编码的句子生成提取摘要