Enriching and Controlling Global Semantics for Text Summarization
This work addresses the issue of missing key points in summaries for users of text summarization systems, representing an incremental improvement over existing methods.
The paper tackles the short-range dependency problem in Transformer-based abstractive summarization by introducing a neural topic model with normalizing flow to capture global semantics and a control mechanism to integrate them effectively, resulting in state-of-the-art performance on five datasets including CNN/DailyMail and XSum.
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.