Daniel Braun is an assistant professor at the Department of Industrial Engineering and Business Information Systems (IEBIS) at the University of Twente. Previously, he worked in the Department of Informatics at the Technical University of Munich, where he also received his PhD. Daniel holds a master's degree in Computing Science from the University of Aberdeen and a bachelor's in Computer Science from Saarland University.

His research focuses on the application of Natural Language Processing (NLP) and Artificial Intelligence (AI) in knowledge-intensive processes, with a focus on problems from the legal domain (Legal Tech). More broadly, he is also interested in the general application of NLP and Natural Language Generation (NLG) in the context of businesses and administration.



Efficient Black-Box Adversarial Attacks on Neural Text DetectorsIn Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP-2023) (pp. 78-83). Association for Computational Linguistics (ACL). Fishchuk, V. & Braun, D.https://aclanthology.org/2023.icnlsp-1.8I beg to differ: how disagreement is handled in the annotation of legal machine learning data setsArtificial intelligence and law (E-pub ahead of print/First online). Braun, D.https://doi.org/10.1007/s10506-023-09369-4Students' Perspective on AI-Supported Assessment of Open-Ended Questions in Higher EducationIn Proceedings of the 15th International Conference on Computer Supported Education (pp. 73-79). Braun., D., Rogetzer., P., Stoica, E. & Kurzhals, H.https://doi.org/10.5220/0011648900003470Investigating Conversational Search Behavior for Domain ExplorationIn Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2‚Äď6, 2023, Proceedings, Part II (pp. 608-616). Springer Nature. Schneider, P., Afzal, A., Vladika, J., Braun, D. & Matthes, F.https://doi.org/10.1007/978-3-031-28238-6_52Semantic Label Representations with¬†Lbl2Vec: A Similarity-Based Approach for¬†Unsupervised Text ClassificationIn Web Information Systems and Technologies: 16th International Conference, WEBIST 2020, November 3‚Äď5, 2020, and 17th International Conference, WEBIST 2021, October 26‚Äď28, 2021, Virtual Events, Revised Selected Papers (pp. 59-73). Springer. Schopf, T., Braun, D. & Matthes, F.https://doi.org/10.1007/978-3-031-24197-0_4Challenges in Domain-Specific Abstractive Summarization and How to Overcome ThemIn Proceedings of the 15th International Conference on Agents and Artificial Intelligence - (Volume 3) (pp. 682-689). SCITEPRESS. Afzal, A., Vladika, J., Braun, D. & Matthes, F.https://doi.org/10.5220/0011744500003393
Tracking Semantic Shifts in German Court Decisions with Diachronic Word EmbeddingsIn Proceedings of the Natural Legal Language Processing Workshop 2022 (pp. 218-227). Association for Computational Linguistics (ACL). Braun, D.https://aclanthology.org/2022.nllp-1.19.pdfEvaluating Unsupervised Text Classification: Zero-Shot and Similarity-Based ApproachesIn Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval (pp. 6–15). Association for Computing Machinery. Schopf, T., Braun, D. & Matthes, F.https://doi.org/10.1145/3582768.3582795Structured Extraction of Terms and Conditions from German and English Online ShopsIn Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) (pp. 181-190). Association for Computational Linguistics (ACL). Schamel, T., Braun, D. & Matthes, F.https://aclanthology.org/2022.ecnlp-1.21Clause Topic Classification in German and English Standard Form ContractsIn Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) (pp. 199-209). Association for Computational Linguistics (ACL). Braun, D. & Matthes, F.https://aclanthology.org/2022.ecnlp-1.23
Automatic Detection of Terms and Conditions in German and English Online ShopsIn Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST 2020, Budapest, Hungary, November 3-5, 2020 (pp. 233-237). SCITEPRESS. Braun, D. & Matthes, F.https://doi.org/10.5220/0010154302330237Would You Lie To Me Bot? Supporting Decision-Making Processes with Deceiving Virtual AgentsProcedia computer science, 177, 587-592. Braun, D., Bhat, M., Biesdorf, A. & Matthes, F.https://doi.org/10.1016/j.procs.2020.10.083MucLex: A German Lexicon for Surface RealisationIn Proceedings of The 12th Language Resources and Evaluation Conference (LREC 2020), Marseille, 11-16 May 2020 (pp. 4653-4657). European Language Resources Association (ELRA). Klimt, K., Braun, D., Schneider, D. & Matthes, F.User-adaptable Natural Language Generation for Regression Testing within the Finance DomainIn Proceedings of the 22nd International Conference on Enterprise Information Systems (pp. 613-618). SCITEPRESS. Braun, D., Sajwan, A. & Matthes, F.https://doi.org/10.5220/0009563306130618

Research profiles

Supervision of Bachelor thesis projects (1st or 2nd Supervision):

  • Alexandru Matcov,¬†Explainable AI in Credit Risk Assessment for External Customers, BIT, 2024
  • Mengmeng Li, The Dual Role of AI in Cybersecurity and Cybersafety: Insights from ChatGPT, BIT, 2024
  • Kaleb Dan, The optimization of the requesting procedure, IEM, 2023
  • Iris te Koppele,¬†Dashboard on lead times, IEM, 2023
  • Vitalii Fishchuk, Adversarial attacks on neural text detectors, BIT, 2023
  • Vlad-Gabriel Stoian,¬†Students' Trust in Automated Grading Through Explainable AI Visualizations, BIT, 2023
  • Ruta Ergle, Enabling the eCMR signing process by implementing e-signature software for Vervo, IEM, 2023
  • Jelmer Hofman, Transparency in AI-driven Grading Tools for Open-ended Questions in Higher Education, BIT, 2023
  • Dani√ęl van Horn,¬†Process improvement using machine learning, IEM, 2022
  • Hugo Vaatstra, Optimising the safety of crossing railroads to aim for a safer rail net, IEM, 2022
  • Max Miedema, Creating insights, KPIs, visualizations and a dashboard of the operational residual streams‚Äô performance regarding the circularity goals of Company X, IEM, 2022
  • Nils Idema, Improving the utilisation of AI Ops analyses by improving the quality of incident logging data, IEM, 2022
  • Ramish¬†Bhutto,¬†Automating Privacy Policy Extraction And Summarization, CS, 2022
  • Eva Stoica,¬†A student‚Äôs take on challenges of AI-driven grading in higher education, BIT, 2022
  • Kristians Balickis,¬†Influence of human-in-the-loop on the acceptance of AI-driven evaluation of essay questions by students, IBA, 2022
  • Anastasia¬†Coviliac,¬†Improvement of AI-assessment systems in grading open questions based on the teaching assistant‚Äôs view, IBA, 2022
  • Henry Kurzhals, Challenges and approaches related to AI-driven grading of open exam questions in higher education : human in the loop, IBA, 2022
  • Rozemarijn van de Leur, Challenges and approaches related to AI-driven grading in higher education: the procedural trust of students, IBA, 2022
  • Alex¬†Kijk in de Vegte,¬†The impact of product Y of company X on corporate social responsibility, IEM, 2022


Supervision of Master thesis projects (1st or 2nd Supervision):

  • Jin Xu,¬†Leveraging Disagreement among Annotators for Text Classification, CS, 2024
  • Zhenqi¬†Zhao,¬†Exploring Lexical Alignment Influences in Price Bargain Chatbot, BIT, 2024
  • Robert Brouwer,¬†Differentiating user groups within an educational dashboard using log data, BIT, 2023
  • Simon Chamoun,¬†Unleashing the potential of Business Intelligence & Analytics for SMEs in the Netherlands: A Comprehensive Analysis, BA, 2023

Affiliated study programs

Courses academic year 2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2022/2023

Current projects

Finished projects

Software Aided Analysis of Terms of Services (SaToS)

The phrase "I have read and understood the terms of service" is often referred to as the biggest lie on the internet. Every time we buy something online, we are confronted with Terms of Services (ToS). However, only a few people actually read these terms, before accepting them, often to their disadvantage. General terms and conditions included in standard form contracts are of significant economic value, as most companies use these terms when entering into contractual relationships with their customers. The interdisciplinary computer and legal science research project SaToS (Software Aided Analysis of ToS) from the chair of Software Engineering for Business Information Systems (sebis) at TU Munich aims to automatically identify Terms of Services and summarise them with regard to their lawfulness and customer friendliness, in a simplified language. In this way, SaToS aims to empower customers to make educated decisions about where to buy or not within seconds, directly addressing the imbalance of powers and fostering the constitutional principle of legal clarity.

Meta Model based Natural Language Generation for Automatic Abstractive Text Summarization (A-SUM)

More than 5000 years after its invention, written language is still the most important medium to document and communicate knowledge. While the production of texts is simplified and accelerated by word processing software, template systems and other technologies, the consumption of texts is still a comparably little by technology supported process. The current state-of-the-art in automatic text summarization are mostly so called extractive methods, which extract the n most important sentences of a text by the means of metrics like TF/IDF. These summaries consist of mostly incoherent sentences. The goal of the BMBF founded Software Campus Project "Meta Model based Natural Language Generation for Automatic Abstractive Text Summarization" (A-SUM) is the creation of coherent, personalized automatic abstractive summaries. Instead of just extracting sentences, A-SUM aims to extract facts from texts and store them in an intermediate, meta model based, structured format. Based on user preferences and contextual information, this information can be personalized. By applying Natural Language Generation techniques, we aim to transform the structured representation of information back to a coherent text, which gives the recipient a quick and tailored overview of the content of the original text.

Vertical Social Software (VSS)

Contextual and Social Computing are converging. Three major trends are impacting software business and engineering: The rise of the verticals In the slipstream of various successful, large horizontal (i.e. generic) social networks such as Facebook, a plethora of niche vertical social websites and software has emerged to provide tailored services and value for niches and vertical domains (e.g. PatientsLikeMe, Stackoverflow or LinkedIn). The number and diversity of devices is growing Nowadays, end-user software is running on ever more types of connected devices from smartphones to smartwatches, augmented/virtual reality headsets etc. Platform business models and technology profoundly impact(ed) the economics of software development The Appeconomy enables third-party developers to target a world-wide audience at much lower costs than few years ago. The "app" construct popularized the idea of having a flexible set of small loosely coupled software modules for specific use-cases tailored to the user's evolving needs.

Technology Scouting as a Service (TSaaS)

The goal is to create a new process for innovation in industrial companies using a Software as a Service platform based on AI algorithms. This enables companies to identify their own customized technology gamechanger faster, cheaper and more efficiently and at the same time to build up internal technology know-how. This offers especially small and medium sized companies without their own research or innovation department an opportunity to keep up with large corporations and to compete. In this way, the project specifically strengthens small and medium-sized enterprises, the backbone of industry, and helps German companies to continue to expand their top position as world market leaders. The innovation of the project and central aspect is the research of a cognitive system in the form of Natural Language Processing (NLP) algorithms linked to an information model consisting of a knowledge graph. This system enables engineers to find technological solutions for domain-specific problems with just a single mouse click. The novelty consists of two core components: Development of an NLP system for the semantic analysis of texts on technologies and problems of mechanical and plant engineering. Development of a knowledge graph in structure and content, which describes and intelligently links the multitude of problems and technologies in mechanical and plant engineering.

AI-Supported Legal Review of Terms and Conditions to Strengthen Consumer Protection (AGB-Check)

The advancing digitization is increasingly influencing everyday work in so-called ‚Äúknowledge-intensive‚ÄĚ professions. Software that supports experts in dealing with legal texts and questions is often summarized under the term "Legal Tech". In theory, it can help make legal expertise available more quickly and cheaply, and hence making it more accessible, especially for consumers. In practice, however, the necessary technologies are often only available to commercial actors. The goal of AGB-Check is the development of innovative technologies that support legal knowledge workers in their efforts to represent the interests of consumer in the digital age better and more effectively. Using the example of a specific application - automated legal reviews of terms and conditions - we want to research technologies that: Support experts in dealing with German legal texts by automatically analyzing them semantically and classify them regarding their legality Enables geographically and organizationally separated experts to participate together in the learning and development process of legal expert systems Allow to combine machine learning and rule-based techniques to process German legal texts to in hybrid systems


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