  We present DeepGen, a system deployed at web scale for automatically creating
sponsored search advertisements (ads) for BingAds customers. We leverage
state-of-the-art natural language generation (NLG) models to generate fluent
ads from advertiser's web pages in an abstractive fashion and solve practical
issues such as factuality and inference speed. In addition, our system creates
a customized ad in real-time in response to the user's search query, therefore
highlighting different aspects of the same product based on what the user is
looking for. To achieve this, our system generates a diverse choice of smaller
pieces of the ad ahead of time and, at query time, selects the most relevant
ones to be stitched into a complete ad. We improve generation diversity by
training a controllable NLG model to generate multiple ads for the same web
page highlighting different selling points. Our system design further improves
diversity horizontally by first running an ensemble of generation models
trained with different objectives and then using a diversity sampling algorithm
to pick a diverse subset of generation results for online selection.
Experimental results show the effectiveness of our proposed system design. Our
system is currently deployed in production, serving ${\sim}4\%$ of global ads
served in Bing.
