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Get To The Point: Summarization with Pointer-Generator ...

Get To The Point: Summarization with Pointer-Generator NetworksAbigail SeeStanford J. LiuGoogle D. ManningStanford sequence-to-sequence models haveprovided a viable new approach forab-stractivetext Summarization (meaningthey are not restricted to simply selectingand rearranging passages from the origi-nal text). However, these models have twoshortcomings: they are liable to reproducefactual details inaccurately, and they tendto repeat themselves. In this work we pro-pose a novel architecture that augments thestandard sequence-to-sequence attentionalmodel in two orthogonal ,we use a hybrid Pointer-Generator networkthat can copy words from the source textviapointing, which aids accurate repro-duction of information, while retaining theability to produce novel words through thegenerator.

higher levels of abstraction while avoiding repe-tition) and ultimately more useful. Therefore we apply our model to the recently-introduced CNN/ Daily Mail dataset (Hermann et al.,2015;Nallap-ati et al.,2016), which contains news articles (39 sentences on average) paired with multi-sentence summaries, and show that we outperform the state-

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  Generators, Protein, Peer, Pointer generator

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