Summary of Generating Attractive and Authentic Copywriting From Customer Reviews, by Yu-xiang Lin and Wei-yun Ma
Generating Attractive and Authentic Copywriting from Customer Reviews
by Yu-Xiang Lin, Wei-Yun Ma
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed sequence-to-sequence framework, enhanced with reinforcement learning, aims to generate attractive and authentic copywriting for e-commerce products by leveraging customer reviews. By incorporating practical experiences from real customers, the framework can produce rich and engaging text descriptions that outperform existing baseline and zero-shot large language models like LLaMA-2-chat-7B and GPT-3.5 in terms of attractiveness and faithfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative approach to copywriting generation uses customer reviews as a source of information, providing a richer understanding of products than just relying on product attributes. The framework is designed to produce attractive, authentic, and informative copywriting that can capture the interest of potential buyers and drive sales. |
Keywords
» Artificial intelligence » Gpt » Llama » Reinforcement learning » Zero shot
