Figurative language generation (FLG) is the task of reformulating a given text to include a desired figure of speech, such as a hyperbole, a simile, and several others, while still being faithful to the original context. This is a fundamental, yet challenging task in Natural Language Processing (NLP), which has recently received increased attention due to the promising performance brought by pre-trained language models. Our survey provides a systematic overview of the development of FLG, mostly in English, starting with the description of some common figures of speech, their corresponding generation tasks, and datasets. We then focus on various modelling approaches and assessment strategies, leading us to discussing some challenges in this field, and suggesting some potential directions for future research. To the best of our knowledge, this is the first survey that summarizes the progress of FLG including the most recent development in NLP. We also organize corresponding resources, e.g., article lists and datasets, and make them accessible in an open repository. We hope this survey can help researchers in NLP and related fields to easily track the academic frontier, providing them with a landscape and a roadmap of this area.

比喻语言生成（FLG）的任务是重新表述给定的文本，以包含所需的修辞手法，例如夸张、明喻等，同时仍然忠实于原始上下文。这是自然语言处理（NLP）中一项基本但具有挑战性的任务，由于预训练语言模型带来的良好性能，最近受到越来越多的关注。我们的调查系统地概述了 FLG 的发展，大部分是英文的，首先描述了一些常见的修辞格、其相应的生成任务和数据集。然后，我们重点关注各种建模方法和评估策略，引导我们讨论该领域的一些挑战，并为未来研究提出一些潜在的方向。据我们所知，这是第一个总结 FLG 进展（包括 NLP 最新发展）的调查。我们还组织相应的资源，例如文章列表和数据集，并使其可以在开放存储库中访问。我们希望这项调查能够帮助 NLP 及相关领域的研究人员轻松追踪学术前沿，为他们提供该领域的概况和路线图。