This paper presents a novel unsupervised abstractive summarization method for
opinionated texts. While the basic variational autoencoder-based models assume
a unimodal Gaussian prior for the latent code of sentences, we alternate it
with a recursive Gaussian mixture, where each mixture component corresponds to
the latent code of a topic sentence and is mixed by a tree-structured topic
distribution. By decoding each Gaussian component, we generate sentences with
tree-structured topic guidance, where the root sentence conveys generic
content, and the leaf sentences describe specific topics. Experimental results
demonstrate that the generated topic sentences are appropriate as a summary of
opinionated texts, which are more informative and cover more input contents
than those generated by the recent unsupervised summarization model
(Bra\v{z}inskas et al., 2020). Furthermore, we demonstrate that the variance of
latent Gaussians represents the granularity of sentences, analogous to Gaussian
word embedding (Vilnis and McCallum, 2015).

本文提出了一种基于高斯混合模型的新颖非监督式抽象摘要方法。实验结果表明，该方法生成的主题句适合作为观点文本摘要，并且更具信息量和覆盖面。

通过生成树状主题引导的句子进行无监督抽象意见摘要

Unsupervised Abstractive Opinion Summarization by Generating Sentences  with Tree-Structured Topic Guidance

This work presents a new approach to unsupervised abstractive summarization
based on maximizing a combination of coverage and fluency for a given length
constraint. It introduces a novel method that encourages the inclusion of key
terms from the original document into the summary: key terms are masked out of
the original document and must be filled in by a coverage model using the
current generated summary. A novel unsupervised training procedure leverages
this coverage model along with a fluency model to generate and score summaries.
When tested on popular news summarization datasets, the method outperforms
previous unsupervised methods by more than 2 R-1 points, and approaches results
of competitive supervised methods. Our model attains higher levels of
abstraction with copied passages roughly two times shorter than prior work, and
learns to compress and merge sentences without supervision.

提出了一种新的无监督抽象摘要方法，利用覆盖率和流畅性最大化的组合来进行句子长度控制，通过遮盖原始文档中关键词并将其填充进生成的摘要中来激励包含原始文档中的关键词，利用覆盖率模型和流畅性模型进行无监督训练，可以在流行的新闻摘要数据集上优于以前的无监督方法，同时接近有竞争力的监督方法。我们的模型比以前的工作得到更高程度的信息提取，复制的段落大约是先前的两倍短，并且可以自主学习压缩和合并句子。

摘要回路：学习撰写抽象摘要而不借助例子

The Summary Loop: Learning to Write Abstractive Summaries Without  Examples

We address the problem of unsupervised abstractive summarization of
collections of user generated reviews with self-supervision and control. We
propose a self-supervised setup that considers an individual document as a
target summary for a set of similar documents. This setting makes training
simpler than previous approaches by relying only on standard log-likelihood
loss. We address the problem of hallucinations through the use of control
codes, to steer the generation towards more coherent and relevant
summaries.Finally, we extend the Transformer architecture to allow for multiple
reviews as input. Our benchmarks on two datasets against graph-based and recent
neural abstractive unsupervised models show that our proposed method generates
summaries with a superior quality and relevance.This is confirmed in our human
evaluation which focuses explicitly on the faithfulness of generated summaries
We also provide an ablation study, which shows the importance of the control
setup in controlling hallucinations and achieve high sentiment and topic
alignment of the summaries with the input reviews.

本文介绍了一种自我训练的方法，使用控制代码通过 Transformer 架构将多条评论进行无监督抽象摘要，并通过人工评估表明，该方法生成的摘要具有更高的质量和相关性。