Year: 2020
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Lemberger, Pirmin
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3027013327