Enriching and Controlling Global Semantics for Text Summarization

ArXi:2109.10616v2 Announce Type: replace 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