  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.
