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Self-Supervised Learning for Contextualized Extractive Summarization
時間 2021-01-02
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ACL 2019 Self-Supervised Learning for Contextualized Extractive Summarization github 背景 本文所表述的內容十分的清晰明瞭,同樣也很簡單,即如何使用不同的預訓練策略來提升抽取式摘要任務的效果。作者指出:現有的模型在抽取句子使用交叉熵訓練模型時,往往只考慮了句子級別的信息,並沒有很好的捕獲全局或說是文檔級的信息,因此
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相關文章
1.
論文閱讀筆記《Ranking Sentences for Extractive Summarization with Reinforcement Learning》
2.
Extractive Summarization using Continuous Vector Space Models
3.
Heterogeneous Graph Neural Networks for Extractive Document Summarization
4.
ACL2020 Heterogeneous Graph Neural Networks for Extractive Document Summarization
5.
讀論文2018 ACL A unified model for extractive and abstractive summarization using inconsistency loss
6.
【論文筆記】Heterogeneous Graph Neural Networks for Extractive Document Summarization
7.
SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documen
8.
Fine-tune BERT for Extractive Summarization中文數據集LCSTS復現
9.
#Paper Reading# Neural Extractive Summarization with Side Information
10.
《Contextualized Code Representation Learning for Commit Message Generation》閱讀
>>更多相關文章<<