In this work we explore deep generative models of text in which the latent
representation of a document is itself drawn from a discrete language model
distribution. We formulate a variational auto-encoder for inference in this
model and apply it to the task of compressing sentences. In this application
the generative model first draws a latent summary sentence from a background
language model, and then subsequently draws the observed sentence conditioned
on this latent summary. In our empirical evaluation we show that generative
formulations of both abstractive and extractive compression yield
state-of-the-art results when trained on a large amount of supervised data.
Further, we explore semi-supervised compression scenarios where we show that it
is possible to achieve performance competitive with previously proposed
supervised models while training on a fraction of the supervised data.

通过深度生成模型进行文本压缩，利用离散语言模型分布作为文档的潜在表示，生成模型引入离散语言模型的概念，通过变分自编码器进行推断，本研究在大量监督数据训练的情况下，得出抽象和提取式压缩的最新成果，并探索半监督压缩场景。