AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions
It provides a comprehensive overview for researchers and practitioners in the field, but is incremental as it synthesizes existing knowledge without introducing new methods.
This survey paper summarizes the current state and future directions of AI-powered text generation, focusing on the shift from generating coherent sentences to incorporating personalized traits for diverse content.
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.