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This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR).
Automated snippet generation for online advertising
Stamatina Thomaidou, Ismini Lourentzou, Panagiotis Katsivelis-Perakis, and Michalis Vazirgiannis. 2013 · 2013
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Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 2018
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Generating better search engine text advertisements with deep reinforcement learning
J Weston Hughes, Keng-hao Chang, and Ruofei Zhang. 2019 · 2019
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Quality-sensitive training! social advertisement generation by leveraging user click behavior
Yongzhen Wang, Heng Huang, Yuliang Yan, and Xiaozhong Liu. 2019 · 2019
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Evolutionary product description generation: A dynamic fine-tuning approach leveraging user click behavior
Yongzhen Wang, Jian Wang, Heng Huang, Hongsong Li, and Xiaozhong Liu. 2020 · 2020
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PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization
An empirical study of generating texts for search engine advertising
Hidetaka Kamigaito, Peinan Zhang, Hiroya Takamura, and Manabu Okumura. 2021 · 2021
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Ad headline generation using self-critical masked language model
Yashal Shakti Kanungo, Sumit Negi, and Aruna Rajan. 2021 · 2021
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Contrastive learning with adversarial perturbations for conditional text generation
Seanie Lee, Dong Bok Lee, and Sung Ju Hwang. 2021 · 2021
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Contrastive learning for many-to-many multilingual neural machine translation
Xiao Pan, Mingxuan Wang, Liwei Wu, and Lei Li. 2021 · 2021
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Reinforcing pretrained models for generating attractive text advertisements
Xiting Wang, Xinwei Gu, Jie Cao, Zihua Zhao, Yulan Yan, Bhuvan Middha, and Xing Xie. 2021 · 2021
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A graph-to-sequence learning framework for summarizing opinionated texts
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