Cogalmis, Kevser NurBulut, Ahmet2023-02-212023-02-212022-01-0110.55730/1300-0632.3911https://hdl.handle.net/11443/1675http://dx.doi.org/10.55730/1300-0632.3911We generated advertisement creatives programmatically using deep neural networks. A landing page contains relevant text data, which can be used for generating advertisement creatives, i.e. ads. We treated the ad generation task as a text summarization problem and built a sequence to sequence model. In order to assess the validity of our approach, we conducted experiments on four datasets. Our empirical results showed that our model generated relevant ads on a template-based dataset with moderate hyperparameters. Training the model with more content increased the performance of the model, which we attributed to rigorous hyperparameter tune-up. The choice of word embedding used in the representation of the input altered the model's performance. When the source and the target shared common sequences during training, the model produced the best results.Online advertisingad creative generationdeep learningArticleWOS:000904725600014