COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation
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Computer Science > Computation and Language
Title:COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation
Abstract:Online ads are essential to all businesses and ad headlines are one of their core creative component. Existing methods can generate headlines automatically and also optimize their click-through-rate (CTR) and quality. However, evolving ad formats and changing creative requirements make it difficult to generate optimized & customized headlines. We propose a novel method that uses prefix control tokens along with BART fine-tuning. It yields the highest CTR and also allows users to control the length of generated headlines for use across different ad formats. The method is also flexible and can easily be adapted to other architectures, creative requirements and optimization criteria. Our experiments demonstrate a 25.82% increment in Rouge-L and a 5.82% increment in estimated CTR over previously published strong ad headline generation baseline.
| Comments: | 10 pages, 5 figures, 5 tables. Published in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22). This is the author's accepted version; the definitive Version of Record is available at this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| ACM classes: | I.2.7; H.3.3 |
| Cite as: | arXiv:2607.08071 [cs.CL] |
| (or arXiv:2607.08071v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.08071
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA, pp. 3127-3136 |
| Related DOI: | https://doi.org/10.1145/3534678.3539069
DOI(s) linking to related resources
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Submission history
From: Yashal Shakti Kanungo [view email][v1] Thu, 9 Jul 2026 02:59:04 UTC (3,049 KB)
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