Authors:
Bruno Gutierrez
;
Jonatas Grosman
;
Fernando A. Correia
and
Hélio Lopes
Affiliation:
Department of Informatics, PUC-Rio, Marquês de São Vicente, 225 RDC, 4th floor - Gávea, Rio de Janeiro, Brazil
Keyword(s):
Text Generation, Data Mining, Generative Artificial Intelligence, Large Language Model, E-Commerce.
Abstract:
In e-commerce, product descriptions have a great influence on the shopping experience, informing consumers and facilitating purchases. However, creating good descriptions is labor-intensive, especially for large retailers managing daily product launches. To address this, we propose an automated method for product description generation using customer reviews and a Large Language Model (LLM) in a zero-shot approach. Our three-step process involves (i) extracting valuable sentences from reviews, (ii) selecting informative and diverse content using a graph-based strategy, and (iii) generating descriptions via prompts based on these selected sentences and the product title. For our proposal evaluation, we had the collaboration of 30 evaluators comparing the generated descriptions with the ones given by the sellers. As a result, our method produced descriptions preferred over those provided by sellers, rated as more informative, readable, and relevant. Additionally, a comparison with a li
terature method demonstrated that our approach, supported by statistical testing, results in more effective and preferred descriptions.
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