Automatic Generation of E-Commerce Marketing Text Based on Improved Diffusion Model
[Objective]This paper aims to expand the application of diffusion models in the field of text generation,and to solve the problem of single and redundant information generated by existing models.[Methods]The TextRank algorithm is used to extract keyword information from the original text,and then the keyword information is integrated into a sequence diffusion model(DiffuSeq)to construct a sequence diffusion model(K-DiffuSeq)that integrates keywords.[Results]Compared to the benchmark models,the K-DiffuSeq model has shown an improvement of at least 4.140％in terms of PPL,32.692％in terms of ROUGE,and 1.566％in terms of diversity measure.[Limitations]Only text corpus related to the product was considered,while richer multimodal product information such as images and videos were ignored.[Conclusions]The integration of keywords can effectively improve the performance of marketing text generation models,and this study confirms the potential application of diffusion models in the field of text generation.
Text GenerationDiffusion ModelDiffuSeqKeywords Extraction
NETL
NSTL
万方数据
