閱讀筆記--《Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond》
本文主要是對論文中的算法技巧和創新點進行總結介紹，借鑑了一些博主的文章也有一些自己的理解。 一、論文的基準模型 參考 http://www.javashuo.com/article/p-gialmxna-pv.html Sequence-to-Sequence Seq2Seq是一類End-to-End算法框架，是從序列到序列轉換模型框架，一般是通過Encoder-Decoder（編碼/解碼）模型實
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- 1. Abstractive Text Summarization using Seq2Seq RNNs and Beyond
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相關文章
- 1. Abstractive Text Summarization using Seq2Seq RNNs and Beyond
- 2. 【讀】seq2seq——（3）Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
- 3. 讀論文2018 ACL A unified model for extractive and abstractive summarization using inconsistency loss
- 4. 【論文】A neural attention model for abstractive sentence summarization 閱讀筆記
- 5. （轉）awesome-text-summarization
- 6. Text Summarization 綜述
- 7. [論文閱讀]《A Template-based Abstractive Meeting Summarization》
- 8. 論文閱讀：Keywords-Guided Abstractive Sentence Summarization
- 9. A Neural Attention Model for Abstractive Sentence Summarization 閱讀筆記
- 10. Abstractive Text Classification Using Sequence-to-convolution Neural Networks