  Nowadays, more and more customers browse and purchase products in favor of
using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are
usually inclined to describe redundant and over-informative product titles to
attract attentions from customers, it is important to concisely display short
product titles on limited screen of mobile phones. To address this discrepancy,
previous studies mainly consider textual information of long product titles and
lacks of human-like view during training and evaluation process. In this paper,
we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short
product title generation in E-Commerce, which innovatively incorporates image
information and attribute tags from product, as well as textual information
from original long titles. MM-GAN poses short title generation as a
reinforcement learning process, where the generated titles are evaluated by the
discriminator in a human-like view. Extensive experiments on a large-scale
E-Commerce dataset demonstrate that our algorithm outperforms other
state-of-the-art methods. Moreover, we deploy our model into a real-world
online E-Commerce environment and effectively boost the performance of click
through rate and click conversion rate by 1.66% and 1.87%, respectively.
