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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.

sentences on average) paired with multi-sentence summaries, and show that we outperform the state-of-the-art abstractive system by at least 2 ROUGE points. Our hybrid pointer-generator network facili-tates copying words from the source text via point-ing (Vinyals et al.,2015), which improves accu-racy and handling of OOV words, while retaining

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  Generators, Protein, Rapide, Pointer generator

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