G. Schumann und J. M. Gómez, "Detection of Contradictions and Inconsistencies in German Regulatory Documents" in Proc. 2024 6th International Conference on Natural Language Processing (ICNLP), 2024.
doi: 10.1109/ICNLP60986.2024.10692679

G. Schumann und J. M. Gómex, "Detection of Conflicts, Contradictions and Inconsistencies in Regulatory Documents: A Literature Review" in Proc. 2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA), 2024.
doi: 10.1109/IDSTA62194.2024.10747003

G. Schumann und J. Marx Gómez, "Extraction of Numerical Facts from German Texts to Enrich Internal Audit Data" in Proc. Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, Cham, 2024.
doi: 10.1007/978-3-031-56576-2_16

G. Schumann, J. Awick, und J. M. Gómez, "Natural Language Processing using Federated Learning: A Structured Literature Review" in Proc. 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), 2023.
doi: 10.1109/AIBThings58340.2023.10292481

G. Schumann und J. M. Gómez, "Unsupervised Contradiction Detection using Sentence Transformations" in Proc. 2023 5th International Conference on Natural Language Processing (ICNLP), 2023.
doi: 10.1109/ICNLP58431.2023.00065

J. Awick, G. Schumann, und J. M. Gómez, "Exploring Federated Learning for Data Integration: A Structured Literature Review" in Proc. 2023 International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE), 2023.
doi: 10.1109/BdKCSE59280.2023.10339771

G. Schumann, K. Meyer, und J. M. Gomez, "Query-Based Retrieval of German Regulatory Documents for Internal Auditing Purposes" in Proc. 2022 5th International Conference on Data Science and Information Technology (DSIT), 2022.
doi: 10.1109/DSIT55514.2022.9943943

G. Schumann und J. Marx Gómez, "Natural Language Processing in Internal Auditing--a Structured Literature Review."

G. Schumann, F. Kruse, und J. Nonnenmacher, "A Practice-Oriented, Control-Flow-Based Anomaly Detection Approach for Internal Process Audits" in Proc. International Conference on Service-Oriented Computing, 2020.

M. Beykirch, C. Hilmer, G. Schumann, L. Kölpin, C. Wübbe, M. Mönning, G. Yalcin, K. Lang, T. H. Dam, J. Kathmann, und others, Architektur eines dezentralen, prognosebasierten EnergiehandelsmodellsSpringer.

J. Nonnenmacher, F. Kruse, G. Schumann, und J. Marx Gómez, "Using Autoencoders for Data-Driven Analysis in Internal Auditing" in Proc. Proceedings of the 54th Hawaii International Conference on System Sciences, , p. 5748.