Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward Improving Coherence and Diversity of Slogan Generation   Previous work in slogan generation focused on utilising slogan skeletons
mined from existing slogans. While some generated slogans can be catchy, they
are often not coherent with the company's focus or style across their marketing
communications because the skeletons are mined from other companies' slogans.
We propose a sequence-to-sequence (seq2seq) transformer model to generate
slogans from a brief company description. A naive seq2seq model fine-tuned for
slogan generation is prone to introducing false information. We use company
name delexicalisation and entity masking to alleviate this problem and improve
the generated slogans' quality and truthfulness. Furthermore, we apply
conditional training based on the first words' POS tag to generate
syntactically diverse slogans. Our best model achieved a ROUGE-1/-2/-L F1 score
of 35.58/18.47/33.32. Besides, automatic and human evaluations indicate that
our method generates significantly more factual, diverse and catchy slogans
than strong LSTM and transformer seq2seq baselines.

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The categories of this paper are: eess
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The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
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The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: econ