  Recently, online shopping has gradually become a common way of shopping for
people all over the world. Wonderful merchandise advertisements often attract
more people to buy. These advertisements properly integrate multimodal
multi-structured information of commodities, such as visual spatial information
and fine-grained structure information. However, traditional multimodal text
generation focuses on the conventional description of what existed and
happened, which does not match the requirement of advertisement copywriting in
the real world. Because advertisement copywriting has a vivid language style
and higher requirements of faithfulness. Unfortunately, there is a lack of
reusable evaluation frameworks and a scarcity of datasets. Therefore, we
present a dataset, E-MMAD (e-commercial multimodal multi-structured
advertisement copywriting), which requires, and supports much more detailed
information in text generation. Noticeably, it is one of the largest video
captioning datasets in this field. Accordingly, we propose a baseline method
and faithfulness evaluation metric on the strength of structured information
reasoning to solve the demand in reality on this dataset. It surpasses the
previous methods by a large margin on all metrics. The dataset and method are
coming soon on \url{https://e-mmad.github.io/e-mmad.net/index.html}.
