  Product description generation is a challenging and under-explored task. Most
such work takes a set of product attributes as inputs then generates a
description from scratch in a single pass. However, this widespread paradigm
might be limited when facing the dynamic wishes of users on constraining the
description, such as deleting or adding the content of a user-specified
attribute based on the previous version. To address this challenge, we explore
a new draft-command-edit manner in description generation, leading to the
proposed new task-controllable text editing in E-commerce. More specifically,
we allow systems to receive a command (deleting or adding) from the user and
then generate a description by flexibly modifying the content based on the
previous version. It is easier and more practical to meet the new needs by
modifying previous versions than generating from scratch. Furthermore, we
design a data augmentation method to remedy the low resource challenge in this
task, which contains a model-based and a rule-based strategy to imitate the
edit by humans. To accompany this new task, we present a human-written
draft-command-edit dataset called E-cEdits and a new metric "Attribute Edit".
Our experimental results show that using the new data augmentation method
outperforms baselines to a greater extent in both automatic and human
evaluations.
