# Automatic Controllable Product Copywriting for E-Commerce

arXiv: https://arxiv.org/abs/2206.10103 · HTML: https://mlsanity.com/paper/2206.10103/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [A Distributional Approach to Controlled Text Generation](https://mlsanity.com/paper/2012.11635/) (2020)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://mlsanity.com/paper/1903.12457/) (2019)
- [Word Mover's Embedding: From Word2Vec to Document Embedding](https://mlsanity.com/paper/1811.01713/) (2018)
- [Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2](https://mlsanity.com/paper/2006.01997/) (2020)
- [Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation](https://mlsanity.com/paper/1606.00776/) (2016)
- [Automatic Product Copywriting for E-Commerce](https://mlsanity.com/paper/2112.11915/) (2021)
- [Intelligent Online Selling Point Extraction for E-Commerce Recommendation](https://mlsanity.com/paper/2112.10613/) (2021)
- [Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks](https://mlsanity.com/paper/1807.08000/) (2018)
- [CUSTOM: Aspect-Oriented Product Summarization for E-Commerce](https://mlsanity.com/paper/2108.08010/) (2021)

## Built on by (most cited first, 5 of 5)

- [LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following](https://mlsanity.com/paper/2308.04913/) (2023)
- [LLM-driven Constrained Copy Generation through Iterative Refinement](https://mlsanity.com/paper/2504.10391/) (2025)
- [Generating Attractive and Authentic Copywriting from Customer Reviews](https://mlsanity.com/paper/2404.13906/) (2024)
- [MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization](https://mlsanity.com/paper/2308.11351/) (2023)
- [Modeling Comparative Logical Relation with Contrastive Learning for Text Generation](https://mlsanity.com/paper/2406.09095/) (2024)

## Abstract (arXiv)

Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copywriting aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. As the services provided by e-commerce platforms become diverse, it is necessary to adapt the patterns of automatically-generated descriptions dynamically. In this paper, we report our experience in deploying an E-commerce Prefix-based Controllable Copywriting Generation (EPCCG) system into the JD.com e-commerce product recommendation platform. The development of the system contains two main components: 1) copywriting aspect extraction; 2) weakly supervised aspect labeling; 3) text generation with a prefix-based language model; 4) copywriting quality control. We conduct experiments to validate the effectiveness of the proposed EPCCG. In addition, we introduce the deployed architecture which cooperates with the EPCCG into the real-time JD.com e-commerce recommendation platform and the significant payoff since deployment.