  We introduce a new approach for abstractive text summarization, Topic-Guided
Abstractive Summarization, which calibrates long-range dependencies from
topic-level features with globally salient content. The idea is to incorporate
neural topic modeling with a Transformer-based sequence-to-sequence (seq2seq)
model in a joint learning framework. This design can learn and preserve the
global semantics of the document, which can provide additional contextual
guidance for capturing important ideas of the document, thereby enhancing the
generation of summary. We conduct extensive experiments on two datasets and the
results show that our proposed model outperforms many extractive and
abstractive systems in terms of both ROUGE measurements and human evaluation.
Our code is available at: https://github.com/chz816/tas.
