AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions

TL;DR

This survey reviews the evolution of AI-powered text generation, highlighting technological advancements, shifting research goals towards personalization, and summarizing key models and applications in the field.

Contribution

It provides a comprehensive overview of the current state, models, and applications of text generation, emphasizing recent developments and future directions.

Findings

Deep learning has revolutionized text generation methods.

Personalization is becoming a central focus in recent research.

A wide range of applications from chatbots to content creation are discussed.

Abstract

In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning. New technologies for text generation ranging from template-based methods to neural network-based methods emerged. Meanwhile, the research objectives have also changed from generating smooth and coherent sentences to infusing personalized traits to enrich the diversification of newly generated content. With the rapid development of text generation solutions, one comprehensive survey is urgent to summarize the achievements and track the state of the arts. In this survey paper, we present the general systematical framework, illustrate the widely utilized models and summarize the classic applications of text generation.