Hybrid NLP model for automating fashion product descriptions: integrating transformers and word embeddings

Purpose This study aims to address the challenge of generating accurate and engaging product descriptions for e-commerce platforms, particularly in the fashion domain. It seeks to alleviate the labor-intensive and time-consuming process of manual description writing by leveraging advanced natural language processing (NLP) techniques. Design/methodology/approach The proposed solution integrates GPT-Neo, a transformer model, with the word-embedding model word2vec to automate product description generation. A dataset comprising 14,000 product titles and descriptions was sourced from Noon, a prominent Arabic e-commerce platform, and used to fine-tune the models for specific fashion categories. Findings The results demonstrate that the developed system effectively generates product descriptions based on product titles, achieving a recall rate of 67% and a precision of 72%. These findings validate the system’s potential to reduce manual effort while maintaining description quality. Originality/value This research offers a novel approach to automating product description generation for Arabic e-commerce platforms. It combines state-of-the-art NLP techniques to address a significant bottleneck in the e-commerce industry, contributing to enhanced operational efficiency and scalability. The study’s outcomes also pave the way for further advancements in multilingual NLP applications.