Get to the point: summarization with pointer-generator networks閱讀
簡介： 基於注意力機制的Seq2Seq模型，使用每一步解碼的隱層狀態與編碼器的隱層狀態計算權重，最終得到context向量，利用context向量和解碼器隱層狀態計算輸出概率。這篇文章主要試圖解決問題1、無法準確複述原文細節，無法處理不在詞表中的詞(oov問題) 2、生成的在摘要中存在重複的片段 Pointer-generator network模型結構圖： 主要貢獻點：
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相關文章
- 1. [ACL2017] Get to The Point: Summarization with Pointer-Generator Networks
- 2. 【論文閱讀】Get to the point :summarization with pointer-generator networks
- 3. PGN: 指針生成網絡（Get To The Point: Summarization with Pointer-Generator Networks）
- 4. 指針生成網絡 Pointer-Generator《Get To The Point: Summarization with Pointer-Generator Networks》論文閱讀筆記
- 5. 論文閱讀： Going Deeper with Point Networks
- 6. Get to the pointer：Abstractvie summarization with pointer-generator networks（指向生成器網絡）---2017ACL
- 7. 論文閱讀 《Text Summarization with Pretrained Encoders》
- 8. 論文閱讀筆記：From Image-level to Pixel-level Labeling with Convolutional Networks
- 9. Ranking with Recursive Neural Networks and Its Application to Multi-document Summarization
- 10. 論文--閱讀翻譯筆記-Reducing the Dimensionality of Data with Neural Networks