  In the past decade, automatic product description generation for e-commerce
have witnessed significant advancement. As the services provided by e-commerce
platforms become diverse, it is necessary to dynamically adapt the patterns of
descriptions generated. The selling point of products is an important type of
product description for which the length should be as short as possible while
still conveying key information. In addition, this kind of product description
should be eye-catching to the readers. Currently, product selling points are
normally written by human experts. Thus, the creation and maintenance of these
contents incur high costs. These costs can be significantly reduced if product
selling points can be automatically generated by machines. In this paper, we
report our experience developing and deploying the Intelligent Online Selling
Point Extraction (IOSPE) system to serve the recommendation system in the
JD.com e-commerce platform. Since July 2020, IOSPE has become a core service
for 62 key categories of products (covering more than 4 million products). So
far, it has generated more than 0.1 billion selling points, thereby
significantly scaling up the selling point creation operation and saving human
labour. These IOSPE generated selling points have increased the click-through
rate (CTR) by 1.89\% and the average duration the customers spent on the
products by more than 2.03\% compared to the previous practice, which are
significant improvements for such a large-scale e-commerce platform.
