Draft, Command, and Edit: Controllable Text Editing in E-Commerce

TL;DR

This paper introduces a new controllable text editing task for e-commerce product descriptions, allowing users to modify existing descriptions through commands, supported by a novel dataset, data augmentation methods, and evaluation metrics.

Contribution

It proposes a draft-command-edit framework for product description editing, along with a new dataset, data augmentation strategies, and an evaluation metric tailored for controllable text editing.

Findings

Data augmentation improves editing performance significantly.

The new metric 'Attribute Edit' effectively evaluates editing quality.

Model-based strategies outperform rule-based methods in experiments.

Abstract

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.