论文标题
自动摘要的深度学习模型
Deep Learning Models for Automatic Summarization
论文作者
论文摘要
文本摘要是一项NLP任务，旨在将文本文档转换为较短的任务，同时保持尽可能多的含义。这本教学文章回顾了许多最近的深度学习体系结构，这些结构有助于推进该领域的研究。我们将在指针网络，分层变压器和强化学习的特定应用中讨论。我们假设NLP内的SEQ2SEQ架构和变压器网络的基本知识。
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.
