Heterogeneous Graph Neural Networks for Extractive Document Summarization
異構圖神經網絡用於抽取文檔摘要（2020 ACL） abstract 學習跨句子之間的關係作爲提取文檔摘要過程中的關鍵步驟，已有多種方法被提出來研究該問題。一種直觀的方法是將它們放在基於圖的神經網絡中，該網絡具有更復雜的結構以捕獲句子之間的關係。在本文中，我們提出了一種基於異構圖的神經網絡（HETERSUMGRAPH）用於提取摘要，該網絡包含除句子外的不同粒度級別的語義節點。這些額外的節點充當句子
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相關文章
- 1. ACL2020 Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 2. 【論文筆記】Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 3. Taming Recurrent Neural Networks for Better Summarization
- 4. Graph Neural Networks for Social Recommendation
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- 6. Self-Supervised Learning for Contextualized Extractive Summarization
- 7. 【論文解讀 CIKM 2018 | GEM】Heterogeneous Graph Neural Networks for Malicious Account Detection
- 8. Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
- 9. Extractive Summarization using Continuous Vector Space Models
- 10. Hybrid Graph Neural Networks for Crowd Counting