The writing of digital text documents has become a longer process that usually goes through revision rounds. Document comparison is important for the human reader interested in changes made by the authors. These documents contain structural data using text-centric XML as one of their main storage systems. Current XML diff algorithms are able to represent differences with a limited number of edit operations: insert, delete, move and update. This approach does not fit the scope of digital text document comparison where the human reader needs to understand actual modifications made by the author. With JATS being a text-centric XML vocabulary, we propose within this paper a new XML diff algorithm called jats-diff, able to support bijection between higher-level modifications made by the authors, such as structural changes and restyling, and the changes detected between XML documents. In addition, jats-diff provides similarity information between different nodes in order to measure the impact of the text changes on the XML tree.

XML is currently a well established and widely used document format. It is used as a core data container in collaborative writing suites and other modern information architectures. The extraction and analysis of differences between two XML document versions is an attractive topic, and has already been tackled by several research groups. The goal of this study is to compare 12 existing state-of-the-art and commercial XML diff algorithms by applying them to JATS documents in order to extract and evaluate changes between two versions of the same academic article. Understanding changes between two article versions is important not only regarding data, but also semantics. Change information consumers in our case are editorial teams, and thus they are more generally interested in change semantics than in the exact data changes. The existing algorithms are evaluated on the following aspects: their edit detection suitability for both text and tree changes, execution speed, memory usage and delta file size. The evaluation process is supported by a Python tool available on Github.

In recent years, there has been a constant increase in the number of scientific peer-reviewed articles published. Each of these articles has to go through a laborious process, from peer review, through author revision rounds, to the final decision made by the editor-in-chief. Lacking time and being under pressure with diverse research tasks, senior scientists need new tools to automate parts of their activities. In this paper, we propose a new approach based on named entity recognition that is able to annotate review comments in order to extract meaningful information about changes requested by reviewers. This research focuses on deep learning models that are achieving state-of-the-art results in many natural language processing tasks. Exploring the performance of BERT-based and XLNet models on the review comments annotation task, a “review-annotation“ model based on SciBERT was trained, able to achieve an F1 score of 0.87. Its usage allows different players in the academic publishing process to better understand the review request. In addition, the correlation of the requested and the actual changes is made possible, allowing the final decision-maker to strengthen the article evaluation.

While citations are essential to scholarly communication, their role in assessing academic success has contributed to various forms of misuse. Citation cartels and citation padding are examples of such misuse, yielding irrelevant references and corrupting the academic record. Here, we introduce a tool that we developed and use internally for detecting problematic citation behaviors, called Cite Lens (sample code available on GitHub: https://github.com/MDPI-AG/citelens.), which analyzes citations using vector (embedding) similarity. This tool can either detect misalignment between an article and its references (article–reference similarity), or the reference and the paragraph in which it is cited (context–reference similarity). We analyze the citation patterns across multiple publishers and topics and show the capability of this approach to detect problematic citations. This tool aims to support MDPI’s editorial screening and help prevent unethical or manipulative citation behavior.

Contribution à l’optimisation d’un processus de publication scientifique: Le nombre d'articles scientifiques n'a cessé d'augmenter ces dernières années. En plus de devoir publier, les scientifiques sont également invités à examiner des articles par les pairs et sont membres de comités de revues où ils sont chargés de prendre des décisions concernant l'acceptation d'articles. Nous proposons dans cette thèse un ensemble d'outils à utiliser par les auteurs, les relecteurs et les éditeurs dans le but d'automatiser une partie de leurs activités. Ces outils sont basés sur deux domaines de recherche : la comparaison de documents et la reconnaissance d’entités nommées (NER). En ce qui concerne la comparaison de documents, 12 outils de comparaison XML existants ont été évalués quant à leurs capacités de comparer des articles. La plupart de ces outils ne prennent en charge que la détection de modifications de bas niveau, un nouvel algorithme de comparaison XML,appelé jats-diff, a été développé et permet de détecter les modifications effectuées par les auteurs lors de la révision de l'article. Quant au NER, cinq modèles d'apprentissage profond ont été évalués et le modèle "review-annotation" basé sur SciBERT a été entraîné. L'utilisation de ce modèle permet d'analyser les commentaires des relecteurs et d'extraire des informations significatives sur les corrections qui ont été demandées aux auteurs. Enfin, la corrélation des modifications effectuées par les auteurs (jats-diff) avec les informations sur les corrections demandées par les relecteurs(review-annotation) a été réalisée. Ces informations combinées permettront d'assister la prise de décision de l'éditeur dans la révision d’articles scientifiques.

Ethicality is a one-stop-shop AI toolset that embeds ethical guardrails into the academic publishing workflow. By detecting misconduct signals at submission and keeping humans in the loop, it shifts publishers from reactive investigation to proactive prevention, strengthening research integrity, editorial efficiency, and trust at scale.