Slogans play a crucial role in building the brand's identity of the firm. A slogan is expected to reflect firm's vision and brand's value propositions in memorable and likeable ways. Automating the generation of slogans with such characteristics is challenging. Previous studies developted and tested slogan generation with syntactic control and summarization models which are not capable of generating distinctive slogans. We introduce a a novel apporach that leverages pre-trained transformer T5 model with noise perturbation on newly proposed 1:N matching pair dataset. This approach serves as a contributing fator in generting distinctive and coherent slogans. Turthermore, the proposed approach incorporates descriptions about the firm and brand into the generation of slogans. We evaluate generated slogans based on ROUGE1, ROUGEL and Cosine Similarity metrics and also assess them with human subjects in terms of slogan's distinctiveness, coherence, and fluency. The results demonstrate that our approach yields better performance than baseline models and other transformer-based models.

翻译：标语在构建企业品牌身份中起着关键作用。优秀的标语应当以令人难忘且讨喜的方式体现企业愿景与品牌价值主张。自动化生成具有此类特征的标语极具挑战性。既有研究开发并测试了基于句法控制和摘要模型的标语生成方法，但难以生成富有特色的标语。本文提出一种创新方法——在新型1:N匹配对数据集上，通过噪声扰动技术对预训练Transformer T5模型进行微调。该方法可有效生成兼具独特性和连贯性的标语。此外，该方案将企业与品牌的描述信息融入标语生成过程。我们通过ROUGE-1、ROUGE-L和余弦相似度指标评估生成标语质量，并从独特性、连贯性和流畅性三个维度开展人工评测。实验结果表明，本方法在各项指标上均优于基线模型及其他基于Transformer的模型。