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Short summary

Researchers propose using RL policy gradient methods on transformer-based masked language models to generate e-commerce advertising headlines from retail content. The model jointly conditions on multiple products a seller wishes to advertise and outperforms existing transformer and LSTM+RL methods in overlap metrics and quality audits. Model-generated headlines also surpass human-submitted headlines in grammar and creative quality as determined by audits.

• •RL policy gradients applied to masked language models for ad headline generation
• •Model jointly conditions on multiple products for advertising headlines
• •Outperforms human-submitted headlines in grammar and creative quality audits

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