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Deep Learning Models for Automatic Summarization

2020-05-25 · Pirmin Lemberger

Text summarization is an NLP task which aims to convert a textual document into a shorter one while keeping as much meaning as possible. This pedagogical article reviews a number of recent Deep Learning architectures that have helped to advance research in this field. We will discuss in particular applications of pointer networks, hierarchical Transformers and Reinforcement Learning. We assume basic knowledge of Seq2Seq architecture and Transformer networks within NLP.

📄 PDF Abstract BibTeX arXiv:2005.11988

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Deep Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Text Summarization

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…
Position-Wise Feed-Forward Layer 설명 없음
Sigmoid Activation 설명 없음
Tanh Activation 설명 없음
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…
Residual Connection 설명 없음
Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

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