{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T09:11:12Z","timestamp":1759828272557,"version":"build-2065373602"},"reference-count":80,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["MAKE"],"abstract":"<jats:p>(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A \u2018transparent\u2019 model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in graph-to-text generation with Large Language Models (LLMs) have made it possible to transform conceptual models into textual reports consisting of coherent and faithful paragraphs, turning a large conceptual model into a very lengthy report would only displace the challenge. (2) Methods: We experimentally examine the implications of two possible approaches: asking the text generator to simplify the model, either via abstractive (LLMs) or extractive summarization, or simplifying the model through graph algorithms and then generating the complete text. (3) Results: We find that the two approaches have similar scores on text-based evaluation metrics including readability and overlap scores (ROUGE, BLEU, Meteor), but faithfulness can be lower when the text generator decides on what is an interesting fact and is tasked with creating a story. These automated metrics capture textual properties, but they do not assess actual user comprehension, which would require an experimental study with human readers. (4) Conclusions: Our results suggest that graph algorithms may be preferable to support modelers in scientific translations from models to text while minimizing hallucinations.<\/jats:p>","DOI":"10.3390\/make7040116","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T08:05:36Z","timestamp":1759824336000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Faithful Narratives from Complex Conceptual Models: Should Modelers or Large Language Models Simplify Causal Maps?"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6682-5483","authenticated-orcid":false,"given":"Tyler J.","family":"Gandee","sequence":"first","affiliation":[{"name":"Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6816-355X","authenticated-orcid":false,"given":"Philippe J.","family":"Giabbanelli","sequence":"additional","affiliation":[{"name":"Virginia Modeling, Analysis, and Simulation Center (VMASC), Old Dominion University, Norfolk, VA 23435, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1080\/17477778.2020.1821587","article-title":"Model credibility revisited: Concepts and considerations for appropriate trust","volume":"16","author":"Yilmaz","year":"2022","journal-title":"J. Simul."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.18564\/jasss.5505","article-title":"[In] Credible Models\u2013Verification, Validation & Accreditation of Agent-Based Models to Support Policy-Making","volume":"27","author":"Belfrage","year":"2024","journal-title":"JASSS J. Artif. Soc. Soc. Simul."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bitencourt, J., Osho, J., Wooley, A., and Harris, G. (2025). Do you trust digital twins? A framework to support the development of trusted digital twins through verification and validation. Int. J. Prod. Res., 1\u201321.","DOI":"10.1080\/00207543.2025.2524516"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.ejor.2020.06.043","article-title":"Facets of trust in simulation studies","volume":"289","author":"Harper","year":"2021","journal-title":"Eur. J. Oper. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Harper, A., Mustafee, N., and Yearworth, M. (2022, January 11\u201314). The issue of trust and implementation of results in healthcare modeling and simulation studies. Proceedings of the 2022 Winter Simulation Conference (WSC), Singapore.","DOI":"10.1109\/WSC57314.2022.10015276"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nguyen, L.K.N., Kumar, C., Jiang, B., and Zimmermann, N. (2023). Implementation of systems thinking in public policy: A systematic review. Systems, 11.","DOI":"10.3390\/systems11020064"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ecolmodel.2014.01.018","article-title":"Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE","volume":"280","author":"Grimm","year":"2014","journal-title":"Ecol. Model."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1177\/0272989X12454579","article-title":"Model transparency and validation: A report of the ISPOR-SMDM Modeling Good Research Practices Task Force\u20137","volume":"32","author":"Eddy","year":"2012","journal-title":"Med Decis. Mak."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1007\/s12525-022-00593-5","article-title":"The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study","volume":"32","author":"Wanner","year":"2022","journal-title":"Electron. Mark."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102268","DOI":"10.1016\/j.simpat.2020.102268","article-title":"The application of modeling and simulation to public health: Assessing the quality of agent-based models for obesity","volume":"108","author":"Giabbanelli","year":"2021","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, L., Deng, T., Zheng, Z., and Shen, Z.J.M. (2021, January 12\u201315). Explainable modeling in digital twin. Proceedings of the 2021 Winter Simulation Conference (WSC), Phoenix, AZ, USA.","DOI":"10.1109\/WSC52266.2021.9715321"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shrestha, A., Mielke, K., Nguyen, T.A., and Giabbanelli, P.J. (2022, January 11\u201314). Automatically explaining a model: Using deep neural networks to generate text from causal maps. Proceedings of the WinterSim, Singapore.","DOI":"10.1109\/WSC57314.2022.10015446"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gandee, T.J., and Giabbanelli, P.J. (2024, January 28\u201331). Combining Natural Language Generation and Graph Algorithms to Explain Causal Maps Through Meaningful Paragraphs. Proceedings of the International Conference on Conceptual Modeling, Pittsburgh, PA, USA.","DOI":"10.1007\/978-3-031-75599-6_25"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Giabbanelli, P., Phatak, A., Mago, V., and Agrawal, A. (2024, January 3\u20136). Narrating Causal Graphs with Large Language Models. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS-57), Honolulu, HI, USA.","DOI":"10.24251\/HICSS.2024.904"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, N., Vergara-Marcillo, C., Diamantopoulos, G., Shen, J., Tziritas, N., Bahsoon, R., and Theodoropoulos, G. (2024, January 6\u20138). Large Language Models for Explainable Decisions in Dynamic Digital Twins. Proceedings of the 5th International Conference on Dynamic Data Driven Applications Systems (DDDAS) 2024, New Brunswick, NJ, USA.","DOI":"10.1007\/978-3-031-94895-4_8"},{"key":"ref_16","unstructured":"Giabbanelli, P.J., and Agrawal, A. (2025, January 6\u20138). Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization. Proceedings of the AAAI Fall Symposium Series on Safe, Ethical, Certified, Uncertainty-aware, Robust, and Explainable AI for Health (SECURE-AI4H), Arlington, VA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Giabbanelli, P.J., Daumas, C., Flandre, N.Y., Pitkar, A., and Vazquez-Estrada, J. (2025). Promoting Empathy in Decision-Making by Turning Agent-Based Models into Stories Using Large-Language Models. J. Simul., 1\u201321.","DOI":"10.1080\/17477778.2025.2536663"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3673226","article-title":"Context, composition, automation, and communication: The C2AC roadmap for modeling and simulation","volume":"34","author":"Uhrmacher","year":"2024","journal-title":"ACM Trans. Model. Comput. Simul."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tolk, A., Clemen, T., Gilbert, N., and Macal, C.M. (2022, January 18\u201320). How can we provide better simulation-based policy support?. Proceedings of the 2022 Annual Modeling and Simulation Conference (ANNSIM), San Diego, CA, USA.","DOI":"10.23919\/ANNSIM55834.2022.9859512"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.forpol.2005.06.006","article-title":"Participatory modeling and analysis for sustainable forest management: Overview of soft system dynamics models and applications","volume":"9","author":"Mendoza","year":"2006","journal-title":"For. Policy Econ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13347-025-00916-2","article-title":"Trust and Transparency in Artificial Intelligence: T. Mitchell","volume":"38","author":"Mitchell","year":"2025","journal-title":"Philos. Technol."},{"key":"ref_22","unstructured":"Herrera, F. (2025). Making Sense of the Unsensible: Reflection, Survey, and Challenges for XAI in Large Language Models Toward Human-Centered AI. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113679","DOI":"10.1016\/j.eswa.2020.113679","article-title":"Automatic text summarization: A comprehensive survey","volume":"165","author":"Salama","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_24","first-page":"1","article-title":"Graph summarization methods and applications: A survey","volume":"51","author":"Liu","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.eswa.2018.11.022","article-title":"MCRMR: Maximum coverage and relevancy with minimal redundancy based multi-document summarization","volume":"120","author":"Verma","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.1016\/S0140-6736(17)31267-9","article-title":"The need for a complex systems model of evidence for public health","volume":"390","author":"Rutter","year":"2017","journal-title":"Lancet"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Russo, F., Broadbent, A., Castellani, B., Fustolo-Gunnink, S., Rod, N.H., Rod, M.H., Moore, S., Rutter, H., Stronks, K., and Uleman, J. (2024). A Pluralistic (Mosaic) Approach to Causality in Health Complexity. The Routledge Handbook of Causality and Causal Methods, Routledge.","DOI":"10.4324\/9781003528937-27"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s12961-022-00835-0","article-title":"A mixed-methods systematic review of suicide prevention interventions involving multisectoral collaborations","volume":"20","author":"Pearce","year":"2022","journal-title":"Health Res. Policy Syst."},{"key":"ref_29","first-page":"34","article-title":"Complex systems methods for impact evaluation: Lessons from the evaluation of an environmental boundary organisation","volume":"28","author":"Reed","year":"2022","journal-title":"Mires Peat"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s12310-019-09354-w","article-title":"Multiple stakeholder perspectives on school-based responses to student suicide risk in a diverse public school district","volume":"12","author":"Kodish","year":"2020","journal-title":"Sch. Ment. Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1002\/9780470776278.ch14","article-title":"Focus groups","volume":"23","author":"Wilkinson","year":"2004","journal-title":"Doing Soc. Psychol. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.envsoft.2018.08.028","article-title":"Tools and methods in participatory modeling: Selecting the right tool for the job","volume":"109","author":"Voinov","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gray, S., Sterling, E.J., Aminpour, P., Goralnik, L., Singer, A., Wei, C., Akabas, S., Jordan, R.C., Giabbanelli, P.J., and Hodbod, J. (2019). Assessing (social-ecological) systems thinking by evaluating cognitive maps. Sustainability, 11.","DOI":"10.3390\/su11205753"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hovmand, P.S. (2013). Group model building and community-based system dynamics process. Community Based System Dynamics, Springer.","DOI":"10.1007\/978-1-4614-8763-0"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1016\/j.envsoft.2010.03.007","article-title":"Modelling with stakeholders","volume":"25","author":"Voinov","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"White, C.T., Mitasova, H., BenDor, T.K., Foy, K., Pala, O., Vukomanovic, J., and Meentemeyer, R.K. (2021). Spatially explicit fuzzy cognitive mapping for participatory modeling of stormwater management. Land, 10.","DOI":"10.3390\/land10111114"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e1781","DOI":"10.1002\/sdr.1781","article-title":"Are we there yet? Saturation analysis as a foundation for confidence in system dynamics modeling, applied to a conceptualization process using qualitative data","volume":"40","author":"Allen","year":"2024","journal-title":"Syst. Dyn. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10584-017-2007-z","article-title":"Assessing impacts and community preparedness to cyclones: A fuzzy cognitive mapping approach","volume":"143","author":"Singh","year":"2017","journal-title":"Clim. Change"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Schuerkamp, R., Giabbanelli, P.J., Grandi, U., and Doutre, S. (2023, January 10\u201313). How to combine models? Principles and mechanisms to aggregate fuzzy cognitive maps. Proceedings of the 2023 Winter Simulation Conference (WSC), San Antonio, TX, USA.","DOI":"10.1109\/WSC60868.2023.10408326"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jort.2015.09.001","article-title":"The structure and function of angler mental models about fish population ecology: The influence of specialization and target species","volume":"12","author":"Gray","year":"2015","journal-title":"J. Outdoor Recreat. Tour."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Giabbanelli, P.J., and Tawfik, A.A. (2020, January 19\u201324). Reducing the gap between the conceptual models of students and experts using graph-based adaptive instructional systems. Proceedings of the International Conference on Human-Computer Interaction, Copenhagen, Denmark.","DOI":"10.1007\/978-3-030-60128-7_40"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Freund, A.J., and Giabbanelli, P.J. (2021, January 19\u201322). Automatically combining conceptual models using semantic and structural information. Proceedings of the 2021 Annual Modeling and Simulation Conference (ANNSIM), Fairfax, VA, USA.","DOI":"10.23919\/ANNSIM52504.2021.9552157"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"S12","DOI":"10.1016\/j.jcjd.2015.01.058","article-title":"Exploring the interactions between physical well-being, and obesity","volume":"39","author":"Drasic","year":"2015","journal-title":"Can. J. Diabetes"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1016\/S0140-6736(07)61740-1","article-title":"Foresight report on obesity","volume":"370","author":"McPherson","year":"2007","journal-title":"Lancet"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Allender, S., Owen, B., Kuhlberg, J., Lowe, J., Nagorcka-Smith, P., Whelan, J., and Bell, C. (2015). A community based systems diagram of obesity causes. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0129683"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1177\/1049732320968779","article-title":"We need to talk about complexity in health research: Findings from a focused ethnography","volume":"31","author":"Papoutsi","year":"2021","journal-title":"Qual. Health Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Giabbanelli, P.J., and Vesuvala, C.X. (2023). Human factors in leveraging systems science to shape public policy for obesity: A usability study. Information, 14.","DOI":"10.3390\/info14030196"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"S13","DOI":"10.1038\/oby.2009.426","article-title":"Implications of the foresight obesity system map for solutions to childhood obesity","volume":"18","author":"Finegood","year":"2010","journal-title":"Obesity"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8622","DOI":"10.1109\/TKDE.2024.3469578","article-title":"Large language models on graphs: A comprehensive survey","volume":"36","author":"Jin","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, J., Yang, Y., Long, W., Xiong, D., Gutierrez-Basulto, V., and Pan, J.Z. (2025). Evaluating and Improving Graph to Text Generation with Large Language Models. arXiv.","DOI":"10.18653\/v1\/2025.naacl-long.513"},{"key":"ref_51","unstructured":"Yuan, S., and Faerber, M. (2023, January 4\u20136). Evaluating Generative Models for Graph-to-Text Generation. Proceedings of the Recent Advances in Natural Language Processing, Varna, Bulgaria."},{"key":"ref_52","unstructured":"Larson, K. (2024, January 3\u20139). A Survey of Graph Meets Large Language Model: Progress and Future Directions. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, Jeju, Republic of Korea."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, S., Zhang, Z., Ma, T., Zhang, C., and Ye, Y. Can LLMs Convert Graphs to Text-Attributed Graphs? In Proceedings of the Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), Albuquerque, NM, USA, 29 April\u20134 May 2025.","DOI":"10.18653\/v1\/2025.naacl-long.65"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Flandre, N.Y., and Giabbanelli, P.J. (2024, January 28\u201331). Can large language models learn conceptual modeling by looking at slide decks and pass graduate examinations? An empirical study. Proceedings of the International Conference on Conceptual Modeling, Pittsburgh, PA, USA.","DOI":"10.1007\/978-3-031-75599-6_15"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"K\u00f6pke, J., and Safan, A. (2024, January 1\u20136). Efficient LLM-based conversational process modeling. Proceedings of the International Conference on Business Process Management, Krakow, Poland.","DOI":"10.1007\/978-3-031-78666-2_20"},{"key":"ref_56","unstructured":"Tel, T., and Minor, M. (July, January 30). Utilizing the Structure of Process Models for Guided Generation of Explanatory Texts. Proceedings of the International Conference on Case-Based Reasoning, Biarritz, France."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kourani, H., Berti, A., Hennrich, J., Kratsch, W., Weidlich, R., Li, C.Y., Arslan, A., Schuster, D., and van der Aalst, W.M. (2024). Leveraging large language models for enhanced process model comprehension. arXiv.","DOI":"10.2139\/ssrn.5017379"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Minor, M., and Kaucher, E. (2024, January 1\u20134). Retrieval augmented generation with LLMs for explaining business process models. Proceedings of the International Conference on Case-Based Reasoning, Merida, Mexico.","DOI":"10.1007\/978-3-031-63646-2_12"},{"key":"ref_59","unstructured":"G\u00fcrtl, S., Schimetta, G., Kerschbaumer, D., Liut, M., and Steinmaurer, A. (2025). Automated Feedback on Student-Generated UML and ER Diagrams Using Large Language Models. arXiv."},{"key":"ref_60","first-page":"45","article-title":"Large Language Models as an Assistant to Interpret UML Models in Model-Based Engineering: An Exploratory Study","volume":"1","author":"Bashiri","year":"2024","journal-title":"Artif. Intell."},{"key":"ref_61","unstructured":"Zhang, S., Zheng, D., Zhang, J., Zhu, Q., Adeshina, S., Faloutsos, C., Karypis, G., and Sun, Y. (2024). Hierarchical compression of text-rich graphs via large language models. arXiv."},{"key":"ref_62","unstructured":"Li, L., Geng, R., Li, B., Ma, C., Yue, Y., Li, B., and Li, Y. (2022). Graph-to-text generation with dynamic structure pruning. arXiv."},{"key":"ref_63","first-page":"277","article-title":"A systematic survey of text summarization: From statistical methods to large language models","volume":"57","author":"Zhang","year":"2024","journal-title":"ACM Computing Surveys"},{"key":"ref_64","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2019). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Daraghmi, E., Atwe, L., and Jaber, A. (2025). A Comparative Study of PEGASUS, BART, and T5 for Text Summarization Across Diverse Datasets. Future Internet, 17.","DOI":"10.3390\/fi17090389"},{"key":"ref_67","unstructured":"Beltagy, I., Peters, M.E., and Cohan, A. (2020). Longformer: The long-document transformer. arXiv."},{"key":"ref_68","unstructured":"Mihalcea, R., and Tarau, P. (2004, January 25\u201326). Textrank: Bringing order into text. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain."},{"key":"ref_69","unstructured":"Inui, K., Jiang, J., Ng, V., and Wan, X. (2019, January 3\u20137). Text Summarization with Pretrained Encoders. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China."},{"key":"ref_70","unstructured":"Lin, C.Y. (2004, January 25\u201326). Rouge: A package for automatic evaluation of summaries. Proceedings of the Text Summarization Branches out, Barcelona, Spain."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., and Zhu, W.J. (2002, January 7\u201312). Bleu: A method for automatic evaluation of machine translation. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA, USA.","DOI":"10.3115\/1073083.1073135"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Edmonds, B., and Moss, S. (2004, January 19). From KISS to KIDS\u2013an \u2018anti-simplistic\u2019modelling approach. Proceedings of the International Workshop on Multi-Agent Systems and Agent-Based Simulation, New York, NY, USA.","DOI":"10.1007\/978-3-540-32243-6_11"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Robinson, S., and Brooks, R. (2024). Assumptions and simplifications in discrete-event simulation modelling. J. Simul., 1\u201318.","DOI":"10.1080\/17477778.2024.2407369"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"van der Zee, D.J. (2017, January 3\u20136). Approaches for simulation model simplification. Proceedings of the 2017 Winter Simulation Conference (WSC), Las Vegas, NV, USA.","DOI":"10.1109\/WSC.2017.8248126"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Freund, A.J., and Giabbanelli, P.J. (2021, January 16\u201318). The necessity and difficulty of navigating uncertainty to develop an individual-level computational model. Proceedings of the International Conference on Computational Science, Krakow, Poland.","DOI":"10.1007\/978-3-030-77980-1_31"},{"key":"ref_76","unstructured":"Hashemi, M., Gong, S., Ni, J., Fan, W., Prakash, B.A., and Jin, W. (2024, January 3\u20139). A comprehensive survey on graph reduction: Sparsification, coarsening, and condensation. Proceedings of the IJCAI \u201924: Thirty-Third International Joint Conference on Artificial Intelligence, Jeju, Republic of Korea."},{"key":"ref_77","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., and Artzi, Y. (2019). Bertscore: Evaluating text generation with bert. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1037\/h0057532","article-title":"A new readability yardstick","volume":"32","author":"Flesch","year":"1948","journal-title":"J. Appl. Psychol."},{"key":"ref_79","unstructured":"Schoonhoven, R., Hendriksen, A.A., Pelt, D.M., and Batenburg, K.J. (2020). LEAN: Graph-based pruning for convolutional neural networks by extracting longest chains. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.3390\/make6040130","article-title":"Node-Centric Pruning: A Novel Graph Reduction Approach","volume":"6","author":"Shokouhinejad","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T08:28:01Z","timestamp":1759825681000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,7]]},"references-count":80,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040116"],"URL":"https:\/\/doi.org\/10.3390\/make7040116","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,7]]}}}