Product copywriting is a critical component of e-commerce recommendation
platforms. It aims to attract users' interest and improve user experience by
highlighting product characteristics with textual descriptions. In this paper,
we report our experience deploying the proposed Automatic Product Copywriting
Generation (APCG) system into the JD.com e-commerce product recommendation
platform. It consists of two main components: 1) natural language generation,
which is built from a transformer-pointer network and a pre-trained
sequence-to-sequence model based on millions of training data from our in-house
platform; and 2) copywriting quality control, which is based on both automatic
evaluation and human screening. For selected domains, the models are trained
and updated daily with the updated training data. In addition, the model is
also used as a real-time writing assistant tool on our live broadcast platform.
The APCG system has been deployed in JD.com since Feb 2021. By Sep 2021, it has
generated 2.53 million product descriptions, and improved the overall averaged
click-through rate (CTR) and the Conversion Rate (CVR) by 4.22% and 3.61%,
compared to baselines, respectively on a year-on-year basis. The accumulated
Gross Merchandise Volume (GMV) made by our system is improved by 213.42%,
compared to the number in Feb 2021.

本文介绍了应用于京东电商产品推荐平台的自动生成商品文案系统的设计与实现，包括自然语言生成和文案质量控制等模块。经过部署，该系统提升了点击率和转化率，累计成交额比部署前增长了 213.42％。