Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to capture the global semantics of the document, which are then integrated into the summarization model. In addition, to avoid the overwhelming effect of global semantics on contextualized representation, we introduce a mechanism to control the amount of global semantics supplied to the text generation module. Our method outperforms state-of-the-art summarization models on five common text summarization datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.

翻译：最近,以变换器为基础的模型通过创造流畅和内容丰富的摘要,在抽象的总结任务中证明是有效的,然而,这些模型仍然受到短期依赖性问题的影响,导致它们产生摘要,没有找到关键文件要点。在本文件中,我们试图通过引入一个神经专题模型来解决这一问题,该模型被赋予正常流权,以捕捉文件的全球语义,然后将其纳入汇总模型。此外,为了避免全球语义对背景化代表的压倒性影响,我们引入了一种机制来控制向文本生成模块提供的全球语义的数量。我们的方法超越了五个通用文本合成数据集(CNN/DailyMail、XSum、Reditit TIFU、ARXiv和PubMed)的拼凑模式。