【論文筆記】Heterogeneous Graph Neural Networks for Extractive Document Summarization
文章使用異質圖建模句子之間的關係，並將其用於抽取式摘要，取得了優於所有對比模型的效果（本文模型和對比模型均沒有使用預訓練語言模型，如BERT）。 圖的結構 圖中包含兩類節點：詞節點和句節點 句節點與其中包含的詞的節點相連接 只有詞節點和句節點之間的連接，同類節點之間不會連接 是一個二分圖（Bipartite Graph） 節點初始化 詞節點 300維的Embedding Vocabulary大小爲
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- 1. Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 2. ACL2020 Heterogeneous Graph Neural Networks for Extractive Document Summarization
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相關文章
- 1. Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 2. ACL2020 Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 3. 論文筆記：KDD 2019 Heterogeneous Graph Neural Network
- 4. 【論文解讀 CIKM 2018 | GEM】Heterogeneous Graph Neural Networks for Malicious Account Detection
- 5. Taming Recurrent Neural Networks for Better Summarization
- 6. 【論文筆記】Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
- 7. 論文閱讀筆記《Ranking Sentences for Extractive Summarization with Reinforcement Learning》
- 8. Learning Convolutional Neural Networks for Graphs論文筆記
- 9. 【論文筆記（2）】3D Graph Neural Networks for RGBD Semantic Segmentation
- 10. 【論文筆記】Heterogeneous Graph Attention Network