Heterogeneous Graph Neural Networks for Extractive Document Summarization
异构图神经网络用于抽取文档摘要（2020 ACL） abstract 学习跨句子之间的关系作为提取文档摘要过程中的关键步骤，已有多种方法被提出来研究该问题。一种直观的方法是将它们放在基于图的神经网络中，该网络具有更复杂的结构以捕获句子之间的关系。在本文中，我们提出了一种基于异构图的神经网络（HETERSUMGRAPH）用于提取摘要，该网络包含除句子外的不同粒度级别的语义节点。这些额外的节点充当句子
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相关文章
- 1. ACL2020 Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 2. 【论文笔记】Heterogeneous Graph Neural Networks for Extractive Document Summarization
- 3. Taming Recurrent Neural Networks for Better Summarization
- 4. Graph Neural Networks for Social Recommendation
- 5. #Paper Reading# Neural Extractive Summarization with Side Information
- 6. Self-Supervised Learning for Contextualized Extractive Summarization
- 7. 【论文解读 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