[ACL2017] Get to The Point: Summarization with Pointer-Generator Networks
目前已有的生成式文本摘要有两个显著的缺点： （1）不准确复制事实细节（复制的事实细节不对） （2）有时候摘要会重复他们本身 针对这两个问题，本文给出了相应的解决方法： （1）pointer-generator模型（指针生成网络), 通过指针从源文本中复制单词以解决OOV问题，并且有能力生成新词（在源文本中没有出现的词） （2）coverage机制：解决seq2seq模型生成句子中经常有重复片段现象
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- 1. 【论文阅读】Get to the point :summarization with pointer-generator networks
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- 3. Get to the point: summarization with pointer-generator networks阅读
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- 5. Get to the pointer：Abstractvie summarization with pointer-generator networks（指向生成器网络）---2017ACL
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相关文章
- 1. 【论文阅读】Get to the point :summarization with pointer-generator networks
- 2. PGN: 指针生成网络（Get To The Point: Summarization with Pointer-Generator Networks）
- 3. Get to the point: summarization with pointer-generator networks阅读
- 4. 指针生成网络 Pointer-Generator《Get To The Point: Summarization with Pointer-Generator Networks》论文阅读笔记
- 5. Get to the pointer：Abstractvie summarization with pointer-generator networks（指向生成器网络）---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
- 9. Find the longest route with the smallest starting point
- 10. Paper：Sequence to Sequence Learning with Neural Networks