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.

翻译：产品文案的目标是通过文本描述强调产品特性，吸引潜在买家的兴趣。随着电子商务平台提供日益多样化的服务，动态调整这些自动生成文案的风格变得至关重要。典型的文案生成方法通常仅依赖指定的产品属性，可能导致内容枯燥且重复。为解决该问题，我们提出基于客户评论生成文案，因为评论提供了产品的第一手实际体验，比产品属性蕴含更丰富的信息。我们开发了增强强化学习的序列到序列框架，用于生成吸引人、真实且信息丰富的文案。该框架在吸引力和忠诚度方面均优于所有现有基线模型和零样本大语言模型（包括LLaMA-2-chat-7B和GPT-3.5）。此外，本工作还利用大语言模型进行基于方面的摘要收集和论点吸引力评估。实验证明了大语言模型在营销领域语料库构建中的有效性。代码和数据集开源地址：https://github.com/YuXiangLin1234/Copywriting-Generation。