Deep Learning Models for Automatic Summarization
It serves as an educational resource for those interested in NLP summarization techniques, but is incremental as it reviews existing methods.
This pedagogical article reviews recent deep learning architectures for automatic text summarization, discussing applications of pointer networks, hierarchical Transformers, and reinforcement learning without presenting new results or numbers.
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.