PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

时间 2021-01-02

标签 NLP 繁體版

原文原文链接

就在这几天微软发布了一款参数量多达170亿的史上最大的自然语言生成模型 Turing-NLG，在多种语言模型基准上均实现了SOTA。值得关注的是Turing-NLG在文本摘要上的表现，由于它已经非常善于理解文本了，因此不需要太多的配对数据就可以实现比已有模型更好的效果。从Facebook的BART、Google的PEGASUS到今天Microsoft的Truing-NLG，越来越大的预训练数据

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1. A Deep Reinforced Model for Abstractive Summarization
2. #Paper Reading# Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
3. Fast Abstractive SUmmarization with Reinforce-Selected Sentence Rewriting
4. Deep Communicating Agents for Abstractive Summarization论文分享
5. PEGASUS
6. #Paper Reading# A Neural Attention Model for Abstractive Sentence Summarization
7. Self-Attention Guided Copy Mechanism for Abstractive Summarization---ACL 2020
8. 多标签分类：Global Encoding for Abstractive Summarization
9. 【读】seq2seq——（2）Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
10. NLG简要调研(data2text+(Abstractive) Summarization)

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