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

arXiv: https://arxiv.org/abs/2208.05623 · HTML: https://mlsanity.com/paper/2208.05623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 11 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [Felix: Flexible Text Editing Through Tagging and Insertion](https://mlsanity.com/paper/2003.10687/) (2020)
- [Neural Syntactic Preordering for Controlled Paraphrase Generation](https://mlsanity.com/paper/2005.02013/) (2020)
- [GLGE: A New General Language Generation Evaluation Benchmark](https://mlsanity.com/paper/2011.11928/) (2020)
- [Text Editing by Command](https://mlsanity.com/paper/2010.12826/) (2020)
- [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://mlsanity.com/paper/1903.12457/) (2019)
- [Unsupervised Paraphrasing via Deep Reinforcement Learning](https://mlsanity.com/paper/2007.02244/) (2020)
- [Meaningful Answer Generation of E-Commerce Question-Answering](https://mlsanity.com/paper/2011.07307/) (2020)
- [Probing Product Description Generation via Posterior Distillation](https://mlsanity.com/paper/2103.01594/) (2021)
- [Fact-based Text Editing](https://mlsanity.com/paper/2007.00916/) (2020)
- [Controllable and Diverse Text Generation in E-commerce](https://mlsanity.com/paper/2102.11497/) (2021)
- [Product Title Generation for Conversational Systems using BERT](https://mlsanity.com/paper/2007.11768/) (2020)

## Abstract (arXiv)

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.