Generating Attractive and Authentic Copywriting from Customer Reviews

Yu-Xiang Lin, Wei-Yun Ma


Abstract
The goal of product copywriting is to capture the interest of potential buyers by emphasizing the features of products through text descriptions. As e-commerce platforms offer a wide range of services, it’s becoming essential to dynamically adjust the styles of these auto-generated descriptions. Typical approaches to copywriting generation often rely solely on specified product attributes, which may result in dull and repetitive content. To tackle this issue, we propose to generate copywriting based on customer reviews, as they provide firsthand practical experiences with products, offering a richer source of information than just product attributes. We have developed a sequence-to-sequence framework, enhanced with reinforcement learning, to produce copywriting that is attractive, authentic, and rich in information. Our framework outperforms all existing baseline and zero-shot large language models, including LLaMA-2-chat-7B and GPT-3.5, in terms of both attractiveness and faithfulness. Furthermore, this work features the use of LLMs for aspect-based summaries collection and argument allure assessment. Experiments demonstrate the effectiveness of using LLMs for marketing domain corpus construction. The code and the dataset is publicly available at: https://github.com/YuXiangLin1234/Copywriting-Generation.
Anthology ID:
2024.naacl-long.259
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4629–4642
Language:
URL:
https://aclanthology.org/2024.naacl-long.259
DOI:
Bibkey:
Cite (ACL):
Yu-Xiang Lin and Wei-Yun Ma. 2024. Generating Attractive and Authentic Copywriting from Customer Reviews. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4629–4642, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Generating Attractive and Authentic Copywriting from Customer Reviews (Lin & Ma, NAACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.naacl-long.259.pdf
Copyright:
 2024.naacl-long.259.copyright.pdf