【论文笔记】Heterogeneous Graph Neural Networks for Extractive Document Summarization
文章使用异质图建模句子之间的关系，并将其用于抽取式摘要，取得了优于所有对比模型的效果（本文模型和对比模型均没有使用预训练语言模型，如BERT）。 图的结构 图中包含两类节点：词节点和句节点 句节点与其中包含的词的节点相连接 只有词节点和句节点之间的连接，同类节点之间不会连接 是一个二分图（Bipartite Graph） 节点初始化 词节点 300维的Embedding Vocabulary大小为
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相关文章
- 1. Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 2. ACL2020 Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 3. 论文笔记：KDD 2019 Heterogeneous Graph Neural Network
- 4. 【论文解读 CIKM 2018 | GEM】Heterogeneous Graph Neural Networks for Malicious Account Detection
- 5. Taming Recurrent Neural Networks for Better Summarization
- 6. 【论文笔记】Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
- 7. 论文阅读笔记《Ranking Sentences for Extractive Summarization with Reinforcement Learning》
- 8. Learning Convolutional Neural Networks for Graphs论文笔记
- 9. 【论文笔记（2）】3D Graph Neural Networks for RGBD Semantic Segmentation
- 10. 【论文笔记】Heterogeneous Graph Attention Network