Get To The Point: Summarization with Pointer-Generator Networks

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1073–1083 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1099 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1073–1083 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1099 Get To The Point: Summarization with Pointer-Generator Networks Abigail See Stanford University abisee@stanford.edu Peter J. Liu Google Brain peterjliu@google.com Christopher D. Manning Stanford University manning@stanford.edu Abstract Neural sequence-to-sequence models have provided a viable new approach for 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 attentional 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 by at least 2 ROUGE points. 1 Introduction Summarization is the task of condensing a piece of text to a shorter version that contains the main in- formation from the original. There are two broad approaches to summarization: extractive and ab- stractive. Extractive methods assemble summaries exclusively from passages (usually whole sen- tences) taken directly from the source text, while abstractive methods may generate novel words and phrases not featured in the source text – as a human-written abstract usually does. The ex- tractive approach is easier, because copying large Original Text (truncated): lagos, nigeria (cnn) a day after winning nige- ria’s presidency, muhammadu buhari told cnn’s christiane amanpour that he plans to aggressively fight corruption that has long plagued nigeria and go after the root of the nation’s unrest. buhari said he’ll “rapidly give attention” to curbing violence in the northeast part of nigeria, where the ter- rorist group boko haram operates. by cooperating with neighboring nations chad, cameroon and niger, he said his administration is confident it will be able to thwart criminals and others contributing to nigeria’s instability. for the first time in nigeria’s history, the opposition defeated the ruling party in democratic elections. buhari defeated incumbent goodluck jonathan by about 2 million votes, according to nigeria’s independent national electoral commission. the win comes after a long history of military rule, coups and botched attempts at democracy in africa’s most populous nation. Baseline Seq2Seq + Attention: UNK UNK says his administration is confi- dent it will be able to destabilize nigeria’s economy. UNK says his admin- istration is confident it will be able to thwart criminals and other nigerians. he says the country has long nigeria and …

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