Ad Headline Generation using Self-Critical Masked Language Model
This paper proposes a programmatic solution to generate advertising headlines for e-commerce using a self-critical masked language model optimized with reinforcement learning policy gradients. The method conditions on multiple products jointly and outperforms existing Transformer and LSTM+RL methods in overlap metrics and quality audits, even surpassing human-written headlines in grammar and creativity.
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[Submitted on 7 Jul 2026]
Title:Ad Headline Generation using Self-Critical Masked Language Model
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Abstract:For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers. It is hard to pass the creative quality bar of the website, especially at a large scale. We thus propose a programmatic solution to generate product advertising headlines using retail content. We propose a state of the art application of Reinforcement Learning (RL) Policy gradient methods on Transformer based Masked Language Models. Our method creates the advertising headline by jointly conditioning on multiple products that a seller wishes to advertise. We demonstrate that our method outperforms existing Transformer and LSTM + RL methods in overlap metrics and quality audits. We also show that our model-generated headlines outperform human submitted headlines in terms of both grammar and creative quality as determined by audits.
Comments: Accepted at NAACL-HLT 2021 (Industry Track). 9 pages, 3 tables, 3 figures - ACL Anthology URL: this https URL - Editors of the proceedings: Young-bum Kim, Yunyao Li, Owen Rambow - Bibkey: kanungo-etal-2021-ad
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.06818 [cs.CL]
(or arXiv:2607.06818v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.06818
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 263-271, June 2021
Related DOI:
https://doi.org/10.18653/v1/2021.naacl-industry.33
DOI(s) linking to related resources
Submission history
From: Yashal Shakti Kanungo [view email] [v1] Tue, 7 Jul 2026 21:25:49 UTC (1,351 KB)
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