The topic-to-essay generation task is a challenging natural language generation task that aims to generate paragraph-level text with high semantic coherence based on a given set of topic words. Previous work has focused on the introduction of external knowledge, ignoring the insufficient generated text diversity. In order to improve the generation diversity, we propose a novel copy mechanism model with a content selection module that integrates rich semantic knowledge from the language model into the decoder. Furthermore, we introduce the improved prefix tuning method to train the model, enabling it to adapt to varying input complexities. In addition, we have contributed a new Chinese dataset for TEG tasks. Experimental results demonstrate that the proposed model can improve the generated text diversity by 35\% to 59\% compared to the state-of-the-art method, while maintaining a high level of topic consistency.

通过引入富语义知识的内容选择模块和改进的前缀调整方法，提出了一种新型的复制机制模型，用于改进语义相干性、生成多样性和主题一致性，并且在TGE任务上的实验结果表明，与现有方法相比，提出的模型可以提高生成文本的多样性35％至59％，同时保持高水平的主题一致性。

基于知识选择的主题到文章生成