The OPTIMICE project devised a method that combines neural machine translation (DeepL) and human post-editing to improve the quality of article metadata (titles, abstracts and keywords) from French to English in the editorial process of journals. In partnership with LIDILE research unit, the PUR (a French publisher) and the MSHB (French Centre for Human Sciences), the translation of the metadata of 16 articles published in 2017 by their authors and by machine translation was first comparatively assessed using our proprietary quality assessment grid. Our goal was to precisely determine the qualitative elements and limitations of each output (human vs. NMT), and design the most appropriate translation method. Professional translators’ assessment confirmed our first results, and helped us formulate recommendations for writing and translating metadata. The recommendations are intended to complement existing instructions to authors, and improve journal acceptance, referencing and international visibility of papers. The method was finally tested on the 2021 issues of the 4 selected journals, focusing respectively on history, archaeology, education and geography. The objective is to develop a methodology for translation that can be reproduced and transferred to other journals, languages and disciplinary fields.