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[ACL2017] Get to The Point: Summarization with Pointer-Generator Networks
時間 2020-12-24
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summarization
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目前已有的生成式文本摘要有兩個顯著的缺點: (1)不準確複製事實細節(複製的事實細節不對) (2)有時候摘要會重複他們本身 針對這兩個問題,本文給出了相應的解決方法: (1)pointer-generator模型(指針生成網絡), 通過指針從源文本中複製單詞以解決OOV問題,並且有能力生成新詞(在源文本中沒有出現的詞) (2)coverage機制:解決seq2seq模型生成句子中經常有重複片段現象
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相關文章
1.
【論文閱讀】Get to the point :summarization with pointer-generator networks
2.
PGN: 指針生成網絡(Get To The Point: Summarization with Pointer-Generator Networks)
3.
Get to the point: summarization with pointer-generator networks閱讀
4.
指針生成網絡 Pointer-Generator《Get To The Point: Summarization with Pointer-Generator Networks》論文閱讀筆記
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Get to the pointer:Abstractvie summarization with pointer-generator networks(指向生成器網絡)---2017ACL
6.
Ranking with Recursive Neural Networks and Its Application to Multi-document Summarization
7.
Taming Recurrent Neural Networks for Better Summarization
8.
#Paper Reading# Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
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Find the longest route with the smallest starting point
10.
Paper:Sequence to Sequence Learning with Neural Networks
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