Generating Attractive and Authentic Copywriting from Customer Reviews (2404.13906v2)

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 LLMs, 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.

• The paper presents a novel framework leveraging customer reviews and reinforcement learning to generate more attractive and authentic e-commerce copywriting.
• The proposed method employs a sequence-to-sequence model optimized by reinforcement learning using reward models for attractiveness, faithfulness, and information density.
• Experiments show this approach outperforms baseline models and zero-shot LLMs in balancing engaging content with fidelity to customer feedback, with code and data publicly available.

The paper "Generating Attractive and Authentic Copywriting from Customer Reviews" presents a novel approach to automated copywriting generation for e-commerce platforms, with a specific focus on leveraging customer reviews. The authors identify a prevalent issue in existing copywriting methods: the over-reliance on predefined product attributes, which can lead to unengaging and repetitive content. To address this limitation, the authors propose a method that utilizes the diverse and authentic experiences found in customer reviews to create more engaging and truthful copywriting.

The proposed framework is based on a sequence-to-sequence modeling approach enhanced with reinforcement learning (RL). This advanced method notably outperforms baseline models and zero-shot LLMs such as LLaMA-2-chat-7B and GPT-3.5 in producing copywriting with enhanced attractiveness and faithfulness. The authors integrate LLMs to facilitate aspect-based summaries collection and evaluate the allure of generated content, demonstrating the models' effectiveness in constructing a corpus tailored to marketing needs.

Key elements of the methodology include:

1. Reinforcement Learning (RL) Integration: The RL framework is used to optimize the model with objectives that balance attractiveness, faithfulness, and information richness of the copywriting.
2. Reward Models: The authors implement three separate reward models to optimize different aspects of the copywriting output:
   • Attractiveness Reward: Learned from pairwise comparisons adjudicated by GPT-3.5, optimized based on win-rate metrics.
   • Faithfulness Reward: Enhanced through textual entailment models to ensure fidelity to the source material.
   • Information Density Reward: Ensures that generated copywriting is information-rich and relevant to existing customer feedback.

The authors conducted comprehensive experiments demonstrating the efficacy of their approach. Their framework showed superior performance in maintaining a balance between attractiveness and truthfulness compared to existing baseline models. The researchers also developed a novel dataset for evaluating restaurant review summarization that incorporates attractiveness scores, showcasing the capability of LLMs in aiding marketing content creation.

The paper not only contributes a unique approach to automated product copywriting but also opens avenues for future research in leveraging customer reviews for marketing purposes. It underscores the importance of integrating consumer insights from reviews to create dynamic and engaging product descriptions that resonate with potential buyers. The framework's code and dataset are made publicly available to encourage further research and development in this area.