In the context of e-commerce, product descriptions have a great influence on the shopping experience. Well-made descriptions should ideally inform a potential consumer about relevant product details, clarifying potential doubt sand facilitating the purchase. Generating good descriptions, however, is a costly activity, which traditionally requires human effort. At the same time, there are a large number of products being launched every day. In this context, this work presents a new methodology for the automated generation of product descriptions, using reviews left by users as a source of information. The proposed method consists of three steps: (i) the extraction of suitable sentences for a description from the reviews (ii) the selection of sentences among the candidates (iii) the generation of the product description from the selected sentences using a Large Language Model (LLM) in a zero-shot way. We evaluate the quality of descriptions generated by our method by comparing them to real product descriptions posted by sellers themselves. In this evaluation, we had the collaboration of 30 evaluators, and we verified that our descriptions are preferred more often than the original descriptions, being considered more informative, readable and relevant. Furthermore, in this same evaluation we replicated a method from recent literature and performed a statistical test comparing its results with our method, and from this comparison we verified that our method generates more informative and preferred descriptions overall.