阅读笔记--《Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond》
本文主要是对论文中的算法技巧和创新点进行总结介绍，借鉴了一些博主的文章也有一些自己的理解。 一、论文的基准模型 参考 http://www.javashuo.com/article/p-gialmxna-pv.html Sequence-to-Sequence Seq2Seq是一类End-to-End算法框架，是从序列到序列转换模型框架，一般是通过Encoder-Decoder（编码/解码）模型实
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相关文章
- 1. Abstractive Text Summarization using Seq2Seq RNNs and Beyond
- 2. 【读】seq2seq——（3）Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
- 3. 读论文2018 ACL A unified model for extractive and abstractive summarization using inconsistency loss
- 4. 【论文】A neural attention model for abstractive sentence summarization 阅读笔记
- 5. （转）awesome-text-summarization
- 6. Text Summarization 综述
- 7. [论文阅读]《A Template-based Abstractive Meeting Summarization》
- 8. 论文阅读：Keywords-Guided Abstractive Sentence Summarization
- 9. A Neural Attention Model for Abstractive Sentence Summarization 阅读笔记
- 10. Abstractive Text Classification Using Sequence-to-convolution Neural Networks