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

可归属且可扩展的意见概括

Attributable and Scalable Opinion Summarization

Opinion summarization provides an important solution for summarizing opinions
expressed among a large number of reviews. However, generating aspect-specific
and general summaries is challenging due to the lack of annotated data. In this
work, we propose two simple yet effective unsupervised approaches to generate
both aspect-specific and general opinion summaries by training on synthetic
datasets constructed with aspect-related review contents. Our first approach,
Seed Words Based Leave-One-Out (SW-LOO), identifies aspect-related portions of
reviews simply by exact-matching aspect seed words and outperforms existing
methods by 3.4 ROUGE-L points on SPACE and 0.5 ROUGE-1 point on OPOSUM+ for
aspect-specific opinion summarization. Our second approach, Natural Language
Inference Based Leave-One-Out (NLI-LOO) identifies aspect-related sentences
utilizing an NLI model in a more general setting without using seed words and
outperforms existing approaches by 1.2 ROUGE-L points on SPACE for
aspect-specific opinion summarization and remains competitive on other metrics.

本文提出两种简单有效的无监督方法来生成针对特定方面和一般意见的摘要，其训练于构建的人工合成数据集上，第一种方法基于原始数据中的方面种子词，第二种方法则是利用自然语言推断模型。