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.

翻译：我们提出一种无监督意见摘要方法，将客户评论中的句子编码到分层离散潜在空间中，然后根据编码频率识别常见观点。通过解码这些高频编码，我们可以生成抽象式摘要；通过选择分配给相同高频编码的句子，即可生成抽取式摘要。该方法具有可归因性，因为模型在摘要生成过程中识别了用于生成摘要的句子。由于聚合操作在潜在空间而非长序列标记上进行，因此该方法可轻松扩展至数百条输入评论。我们还证明，该方法具有一定可控性：通过将模型限制在编码空间中对应特定方面（如位置或饮食）的区域，可生成面向特定方面的摘要。在两个不同领域数据集上的自动评估与人工评估表明，相比现有方法，我们生成的摘要信息量更丰富，且与输入评论的关联性更强。