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

Topic-Guided Abstractive Text Summarization: a Joint Learning Approach