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Get To The Point: Summarization with Pointer-Generator Networks

Abigail See
Christopher Manning
Association for Computational Linguistics (2017)


Neural sequence-to-sequence models have provided a new viable approach to ab- stractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the origi- nal text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we pro- pose a novel architecture that augments the standard sequence-to-sequence atten- tion model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate repro- duction of information while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail sum- marization task, outperforming the current abstractive state-of-the-art ROUGE scores with statistical significance.