Text generation has played an important role in various applications of natural language processing (NLP) In recent studies, researchers are paying increasing attention to modeling and manipulating the style of the generation text . In this tutorial, we will provide a comprehensive literature review in this direction . We start from the definition of style and different settings of stylized text generation, illustrated with various applications . Then, we present different settings, such as style-conditioned generation, style-transfer generation, and style-adversarial generation . In each setting, we delve deep into machine learning methods, including embedding learning techniques to represent style, adversarial learning, and reinforcement learning with cycle consistency to match content but to distinguish different styles . We conclude our tutorial by presenting the challenges of stylizing text generation and discussing future directions, including future directions such as non-

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Keywords : generation - style - text - learning - applications -

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