  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.
