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.