  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.
