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In the last two decades, the landscape of text generation has undergonetremendous changes and is being reshaped by the success of deep learning. Newtechnologies for text generation ranging from template-based methods to neuralnetwork-based methods emerged. Meanwhile, the research objectives have alsochanged from generating smooth and coherent sentences to infusing personalizedtraits to enrich the diversification of newly generated content. With the rapiddevelopment of text generation solutions, one comprehensive survey is urgent tosummarize the achievements and track the state of the arts. In this surveypaper, we present the general systematical framework, illustrate the widelyutilized models and summarize the classic applications of text generation.

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