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。