PGN: 指針生成網絡（Get To The Point: Summarization with Pointer-Generator Networks）
文章目錄 1 引言 2 本文模型 2.1 Seq2Seq 注意力模型 2.2 指針生成網絡 2.3 覆蓋機制（Coverage mechanism） 【Reference】 1. Get To The Point: Summarization with Pointer-Generator Networks seq2seq模型可用於文本摘要（並非簡單地選擇、重排原始文本的段落），然後這些模型有兩個缺
相關文章
- 1. [ACL2017] Get to The Point: Summarization with Pointer-Generator Networks
- 2. 指針生成網絡 Pointer-Generator《Get To The Point: Summarization with Pointer-Generator Networks》論文閱讀筆記
- 3. Get to the pointer：Abstractvie summarization with pointer-generator networks（指向生成器網絡）---2017ACL
- 4. 【論文閱讀】Get to the point :summarization with pointer-generator networks
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- 8. #Paper Reading# Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
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相關文章
- 1. [ACL2017] Get to The Point: Summarization with Pointer-Generator Networks
- 2. 指針生成網絡 Pointer-Generator《Get To The Point: Summarization with Pointer-Generator Networks》論文閱讀筆記
- 3. Get to the pointer：Abstractvie summarization with pointer-generator networks（指向生成器網絡）---2017ACL
- 4. 【論文閱讀】Get to the point :summarization with pointer-generator networks
- 5. Get to the point: summarization with pointer-generator networks閱讀
- 6. Ranking with Recursive Neural Networks and Its Application to Multi-document Summarization
- 7. [生成對抗網絡GAN入門指南]（8）SGAN：Semi-Supervised Learning with Generative Adversarial Networks
- 8. #Paper Reading# Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
- 9. Taming Recurrent Neural Networks for Better Summarization
- 10. 論文淺嘗 | 利用指針生成網絡的知識圖譜自然語言生成