  Previous work in slogan generation focused on generating novel slogans by
utilising templates mined from real slogans. While some such slogans can be
catchy, they are often not coherent with the company's focus or style across
their marketing communications because the templates are mined from other
companies' slogans. We propose a sequence-to-sequence transformer model to
generate slogans from a brief company description. A naive sequence-to-sequence
model fine-tuned for slogan generation is prone to introducing false
information, especially unrelated company names appearing in the training data.
We use delexicalisation to address this problem and improve the generated
slogans' quality by a large margin. Furthermore, we apply two simple but
effective approaches to generate more diverse slogans. Firstly, we train a
slogan generator conditioned on the industry. During inference time, by
changing the industry, we can obtain different "flavours" of slogans. Secondly,
instead of using only the company description as the input sequence, we sample
random paragraphs from the company's website. Surprisingly, the model can
generate meaningful slogans, even if the input sequence does not resemble a
company description. We validate the effectiveness of the proposed method with
both quantitative evaluation and qualitative evaluation. Our best model
achieved a ROUGE-1/-2/-L F1 score of 53.13/33.30/46.49. Besides, human
evaluators assigned the generated slogans an average score of 3.39 on a scale
of 1-5, indicating the system can generate plausible slogans with a quality
close to human-written ones (average score 3.55).
