Automatic Generation Of Agricultural Product Advertising Copy Based On Controlled Text Generation Technology

This paper addresses the importance and challenges of agricultural product advertising copywriting in the natural language generation field.By constructing a specialized domainspecific plain text dataset consisting of over one million agricultural product copywriting examples,a novel controlled text generation algorithm is developed based on BERT and GPT-2 pre-trained language models.The algorithm incorporates the improved Top-k sampling technique and the Markov Chain Monte Carlo(MCMC)sampling algorithm to achieve unsupervised generation of agricultural product copywriting that conforms to user intent.The agricultural product copywriting dataset,constructed using web scraping and Optical Character Recognition(OCR)technology,provides valuable data support for research in the agricultural domain and natural language processing field.The proposed innovative unsupervised controlled text generation algorithm can generate text that meets user intent while ensuring diversity and fluency in the generated results.The algorithm demonstrates good interpretability and debuggability.Experimental results show that the text generation algorithm developed in this study has a clear advantage in generating highquality,demand-specific agricultural product copywriting,with practical application value.It offers a useful reference and basis for agricultural product copywriting generation and related research.