Recent advances of neural text generation: Core tasks, datasets,models and challenges

• 作 者: JIN HanQi CAO Yue WANG TianMing XING XinYu WAN XiaoJun
• 机 构: Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, China;Center for Data
• 出 处: 中国科学:技术科学（英文版）
• 发表日期: 2020年10期
• 关键词: natural language generation neural text generation AMR-to-text data-to-text text

In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and non-linguistic input,and generates natural language text.This survey aims to provide an up-to-date synthesis of core tasks in neural text generation and the architectures adopted to handle these tasks,and draw attention to the challenges in neural text generation.We first outline the mainstream neural text generation frameworks,and then introduce datasets,advanced models and challenges of four core text generation tasks in detail,including AMR-to-text generation,data-to-text generation,and two text-to-text generation tasks (i.e.,text summarization and paraphrase generation).Finally,we present future research directions for neural text generation.This survey can be used as a guide and reference for researchers and practitioners in this area.