Transcription of Get To The Point: Summarization with Pointer-Generator ...
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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.
Germany beat Argentina 2-0 the model may attend to the words victorious and win in the source text. et al.,2014), in which recurrent neural networks (RNNs) both read and freely generate text, has made abstractive summarization viable (Chopra et al.,2016;Nallapati et al.,2016;Rush et al., 2015;Zeng et al.,2016). Though these systems
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