{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:03:50Z","timestamp":1765357430398,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In this research, we present an algorithm that leverages language-transformer technologies to automate the generation of product requirements, utilizing E-Shop consumer reviews as a data source. Our methodology combines classical natural language processing techniques with diverse functions derived from transformer concepts, including keyword and summary generation. To effectively capture the most critical requirements, we employ the opportunity matrix as a robust mechanism for identifying and prioritizing urgent needs. Utilizing transformer technologies, mainly through the implementation of summarization and sentiment analysis, we can extract fundamental requirements from consumer assessments. As a practical demonstration, we apply our technology to analyze the ratings of the Amazon echo dot, showcasing our algorithm\u2019s superiority over conventional approaches by extracting human-readable problem descriptions to identify critical user needs. The results of our study exemplify the potential of transformer-enhanced opportunity mining in advancing the requirements-elicitation processes. Our approach streamlines product improvement by extracting human-readable problem descriptions from E-Shop consumer reviews, augmenting operational efficiency, and facilitating decision-making. These findings underscore the transformative impact of incorporating transformer technologies within requirements engineering, paving the way for more effective and scalable algorithms to elicit and address user needs.<\/jats:p>","DOI":"10.3390\/a16090403","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:23:40Z","timestamp":1692872620000},"page":"403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["From Data to Human-Readable Requirements: Advancing Requirements Elicitation through Language-Transformer-Enhanced Opportunity Mining"],"prefix":"10.3390","volume":"16","author":[{"given":"Pascal","family":"Harth","sequence":"first","affiliation":[{"name":"Institute of IT Management and Digitization Research (IFID), 40476 D\u00fcsseldorf, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6789-7279","authenticated-orcid":false,"given":"Orlando","family":"J\u00e4hde","sequence":"additional","affiliation":[{"name":"Institute of IT Management and Digitization Research (IFID), 40476 D\u00fcsseldorf, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8277-4211","authenticated-orcid":false,"given":"Sophia","family":"Schneider","sequence":"additional","affiliation":[{"name":"Institute of IT Management and Digitization Research (IFID), 40476 D\u00fcsseldorf, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1397-9284","authenticated-orcid":false,"given":"Nils","family":"Horn","sequence":"additional","affiliation":[{"name":"Institute of IT Management and Digitization Research (IFID), 40476 D\u00fcsseldorf, Germany"},{"name":"Faculty of Legal and Business Sciences, Universidad Cat\u00f3lica San Antonio de Murcia, 30107 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4130-9253","authenticated-orcid":false,"given":"R\u00fcdiger","family":"Buchkremer","sequence":"additional","affiliation":[{"name":"Institute of IT Management and Digitization Research (IFID), 40476 D\u00fcsseldorf, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89899","DOI":"10.1109\/ACCESS.2020.2993838","article-title":"Domain Ontology for Requirements Classification in Requirements Engineering Context","volume":"8","author":"Alrumaih","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113751","DOI":"10.1016\/j.dss.2022.113751","article-title":"Sourcing Product Innovation Intelligence from Online Reviews","volume":"157","author":"Goldberg","year":"2022","journal-title":"Decis. Support Syst."},{"doi-asserted-by":"crossref","unstructured":"Lim, S., Henriksson, A., and Zdravkovic, J. (2021). Data-Driven Requirements Elicitation: A Systematic Literature Review, Springer.","key":"ref_3","DOI":"10.1007\/s42979-020-00416-4"},{"doi-asserted-by":"crossref","unstructured":"Horn, N., and Buchkremer, R. (2023, January 6\u20138). The Application of Artificial Intelligence to Elaborate Requirements Elicitation. Proceedings of the 17th International Technology, Education and Development Conference, Valencia, Spain.","key":"ref_4","DOI":"10.21125\/inted.2023.0579"},{"unstructured":"Pohl, K. (2010). Requirements Engineering: Fundamentals, Principles, and Techniques, Springer Publishing Company, Incorporated.","key":"ref_5"},{"unstructured":"Zowghi, D., and Coulin, C. (2005). Engineering and Managing Software Requirements, Springer.","key":"ref_6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1007\/s10270-021-00926-6","article-title":"Holistic Data-Driven Requirements Elicitation in the Big Data Era","volume":"21","author":"Henriksson","year":"2022","journal-title":"Softw. Syst. Model."},{"unstructured":"Surana, C.S.R.K., Gupta, D.B., and Shankar, S.P. (2019, January 17\u201318). Intelligent Chatbot for Requirements Elicitation and Classification. Proceedings of the 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India.","key":"ref_8"},{"doi-asserted-by":"crossref","unstructured":"Khan, J.A., Xie, Y., Liu, L., and Wen, L. (2019, January 23\u201327). Analysis of Requirements-Related Arguments in User Forums. Proceedings of the 2019 IEEE 27th International Requirements Engineering Conference (RE), Jeju Island, Republic of Korea.","key":"ref_9","DOI":"10.1109\/RE.2019.00018"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"011101","DOI":"10.1115\/1.4044435","article-title":"A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems","volume":"142","author":"Zhou","year":"2020","journal-title":"J. Mech. Des."},{"doi-asserted-by":"crossref","unstructured":"G\u00fclle, K.J., Ford, N., Ebel, P., Brokhausen, F., and Vogelsang, A. (2020, January 1). Topic Modeling on User Stories Using Word Mover\u2019s Distance. Proceedings of the 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), Zurich, Switzerland.","key":"ref_11","DOI":"10.1109\/AIRE51212.2020.00015"},{"doi-asserted-by":"crossref","unstructured":"Jiang, W., Ruan, H., Zhang, L., Lew, P., and Jiang, J. (2014, January 13\u201316). For User-Driven Software Evolution: Requirements Elicitation Derived from Mining Online Reviews. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Tainan, Taiwan.","key":"ref_12","DOI":"10.1007\/978-3-319-06605-9_48"},{"doi-asserted-by":"crossref","unstructured":"Mekala, R.R., Irfan, A., Groen, E.C., Porter, A., and Lindvall, M. (2021, January 20\u201324). Classifying User Requirements from Online Feedback in Small Dataset Environments Using Deep Learning. Proceedings of the 2021 IEEE 29th International Requirements Engineering Conference (RE), Notre Dame, IN, USA.","key":"ref_13","DOI":"10.1109\/RE51729.2021.00020"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"084501","DOI":"10.1115\/1.4048960","article-title":"Automated Keyword Filtering in Latent Dirichlet Allocation for Identifying Product Attributes from Online Reviews","volume":"143","author":"Joung","year":"2021","journal-title":"J. Mech. Des."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103253","DOI":"10.1016\/j.jretconser.2022.103253","article-title":"Requirement Analysis and Service Optimization of Multiple Category Fresh Products in Online Retailing Using Importance-Kano Analysis","volume":"72","author":"Zhang","year":"2023","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1509\/jmkr.48.5.881","article-title":"Automated Marketing Research Using Online Customer Reviews","volume":"48","author":"Lee","year":"2011","journal-title":"J. Mark. Res."},{"doi-asserted-by":"crossref","unstructured":"Dafaalla, H., Abaker, M., Abdelmaboud, A., Alghobiri, M., Abdelmotlab, A., Ahmad, N., Eldaw, H., and Hasabelrsoul, A. (2022). Deep Learning Model for Selecting Suitable Requirements Elicitation Techniques. Appl. Sci., 12.","key":"ref_17","DOI":"10.3390\/app12189060"},{"doi-asserted-by":"crossref","unstructured":"Sainani, A., Anish, P.R., Joshi, V., and Ghaisas, S. (September, January 31). Extracting and Classifying Requirements from Software Engineering Contracts. Proceedings of the 2020 IEEE 28th International Requirements Engineering Conference (RE), Zurich, Switzerland.","key":"ref_18","DOI":"10.1109\/RE48521.2020.00026"},{"doi-asserted-by":"crossref","unstructured":"Alturaief, N., Aljamaan, H., and Baslyman, M. (2021, January 15\u201319). AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation. Proceedings of the 2021 36th IEEE\/ACM International Conference on Automated Software Engineering Workshops (ASEW), Melbourne, VIC, Australia.","key":"ref_19","DOI":"10.1109\/ASEW52652.2021.00049"},{"doi-asserted-by":"crossref","unstructured":"Franch, X., Henriksson, A., Ralyt\u00e9, J., and Zdravkovic, J. (2021, January 20\u201324). Data-Driven Agile Requirements Elicitation through the Lenses of Situational Method Engineering. Proceedings of the 2021 IEEE 29th International Requirements Engineering Conference (RE), Notre Dame, IN, USA.","key":"ref_20","DOI":"10.1109\/RE51729.2021.00045"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100889","DOI":"10.1016\/j.elerap.2019.100889","article-title":"Mining User Requirements to Facilitate Mobile App Quality Upgrades with Big Data","volume":"38","author":"Chen","year":"2019","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102102","DOI":"10.1016\/j.ipm.2019.102102","article-title":"Automatic Classification of Complaint Letters According to Service Provider Categories","volume":"56","author":"Dilmon","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s12525-019-00351-0","article-title":"Supporting Customer-Oriented Marketing with Artificial Intelligence: Automatically Quantifying Customer Needs from Social Media","volume":"30","author":"Goutier","year":"2020","journal-title":"Electron. Mark."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s00766-015-0226-2","article-title":"Leveraging Topic Modeling and Part-of-Speech Tagging to Support Combinational Creativity in Requirements Engineering","volume":"20","author":"Bhowmik","year":"2015","journal-title":"Requir. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.ijinfomgt.2017.09.009","article-title":"Social Media Mining for Product Planning: A Product Opportunity Mining Approach Based on Topic Modeling and Sentiment Analysis","volume":"48","author":"Jeong","year":"2019","journal-title":"Int. J. Inf. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1162\/coli_a_00034","article-title":"Opinion Word Expansion and Target Extraction through Double Propagation","volume":"37","author":"Qiu","year":"2011","journal-title":"Comput. Linguist."},{"key":"ref_27","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"179","DOI":"10.3390\/ai2020011","article-title":"Latent Dirichlet Allocation and T-Distributed Stochastic Neighbor Embedding Enhance Scientific Reading Comprehension of Articles Related to Enterprise Architecture","volume":"2","author":"Horn","year":"2021","journal-title":"AI"},{"key":"ref_29","first-page":"147","article-title":"Attractive Quality and Must-Be Quality","volume":"31","author":"Kano","year":"1984","journal-title":"J. Jpn. Soc. Qual. Control"},{"unstructured":"Horn, N., Erhardt, M.S., Di Stefano, M., Bosten, F., and Buchkremer, R. (2020). K\u00fcnstliche Intelligenz in Wirtschaft & Gesellschaft, Springer Fachmedien Wiesbaden.","key":"ref_30"},{"key":"ref_31","first-page":"3111","article-title":"Distributed Representations of Words and Phrases and Their Compositionality","volume":"26","author":"Mikolov","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"unstructured":"Kusner, M.J., Sun, Y., Kolkin, N.I., and Weinberger, K.Q. (2015, January 6\u201311). From Word Embeddings to Document Distances. Proceedings of the 32nd International Conference on Machine Learning, Lille, France.","key":"ref_32"},{"doi-asserted-by":"crossref","unstructured":"Van Vliet, M., Groen, E.C., Dalpiaz, F., and Brinkkemper, S. (2020, January 24\u201327). Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing. Proceedings of the Requirements Engineering: Foundation for Software Quality: 26th International Working Conference, REFSQ 2020, Pisa, Italy. Proceedings 26.","key":"ref_33","DOI":"10.1007\/978-3-030-44429-7_11"},{"unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv.","key":"ref_34"},{"doi-asserted-by":"crossref","unstructured":"Zhao, Z., Zhang, L., Lian, X., Gao, X., Lv, H., and Shi, L. (2023). ReqGen: Keywords-Driven Software Requirements Generation. Mathematics, 11.","key":"ref_35","DOI":"10.3390\/math11020332"},{"doi-asserted-by":"crossref","unstructured":"Dalpiaz, F., Dell\u2019Anna, D., Aydemir, F.B., and \u00c7evikol, S. (2019, January 23\u201327). Requirements Classification with Interpretable Machine Learning and Dependency Parsing. Proceedings of the 2019 IEEE 27th International Requirements Engineering Conference (RE), Jeju Island, South Korea.","key":"ref_36","DOI":"10.1109\/RE.2019.00025"},{"doi-asserted-by":"crossref","unstructured":"Panichella, S., and Ruiz, M. (September, January 31). Requirements-Collector: Automating Requirements Specification from Elicitation Sessions and User Feedback. Proceedings of the 2020 IEEE 28th International Requirements Engineering Conference (RE), Zurich, Switzerland.","key":"ref_37","DOI":"10.1109\/RE48521.2020.00057"},{"doi-asserted-by":"crossref","unstructured":"Abadeer, M., and Sabetzadeh, M. (2021, January 20\u201324). Machine Learning-Based Estimation of Story Points in Agile Development: Industrial Experience and Lessons Learned. Proceedings of the 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), Notre Dame, IN, USA.","key":"ref_38","DOI":"10.1109\/REW53955.2021.00022"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"26926","DOI":"10.1109\/ACCESS.2021.3057807","article-title":"A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays","volume":"9","author":"Rankovic","year":"2021","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Al Qaisi, H., Quba, G.Y., Althunibat, A., Abdallah, A., and Alzu\u2019bi, S. (2021, January 14\u201315). An Intelligent Prototype for Requirements Validation Process Using Machine Learning Algorithms. Proceedings of the 2021 International Conference on Information Technology (ICIT), Amman, Jordan.","key":"ref_40","DOI":"10.1109\/ICIT52682.2021.9491758"},{"doi-asserted-by":"crossref","unstructured":"Osman, M.H., and Zaharin, M.F. (2018, January 2). Ambiguous Software Requirement Specification Detection: An Automated Approach. Proceedings of the 5th International Workshop on Requirements Engineering and Testing, Gothenburg, Germany.","key":"ref_41","DOI":"10.1145\/3195538.3195545"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100997","DOI":"10.1016\/j.cola.2020.100997","article-title":"The Patterns of User Experience for Sticky-Note Diagrams in Software Requirements Workshops","volume":"61","author":"Gardner","year":"2020","journal-title":"J. Comput. Lang."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.cola.2018.12.005","article-title":"Success Factors Influencing Requirements Change Management Process in Global Software Development","volume":"51","author":"Akbar","year":"2019","journal-title":"J. Comput. Lang."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.cola.2019.02.004","article-title":"AutoReq: Expressing and Verifying Requirements for Control Systems","volume":"51","author":"Naumchev","year":"2019","journal-title":"J. Comput. Lang."},{"key":"ref_45","first-page":"83","article-title":"Choosing Scrapy","volume":"31","author":"Myers","year":"2015","journal-title":"J. Comput. Sci. Coll."},{"doi-asserted-by":"crossref","unstructured":"Reimers, N., and Gurevych, I. (2019, January 7). Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks. Proceedings of the EMNLP-IJCNLP 2019\u20142019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Hong Kong, China.","key":"ref_46","DOI":"10.18653\/v1\/D19-1410"},{"doi-asserted-by":"crossref","unstructured":"Goyal, N., Du, J., Ott, M., Anantharaman, G., and Conneau, A. (2021, January 6). Larger-Scale Transformers for Multilingual Masked Language Modeling. Proceedings of the RepL4NLP 2021\u20146th Workshop on Representation Learning for NLP, Online.","key":"ref_47","DOI":"10.18653\/v1\/2021.repl4nlp-1.4"},{"doi-asserted-by":"crossref","unstructured":"Sharma, P., and Li, Y. (2019). Self-Supervised Contextual Keyword and Keyphrase Retrieval with Self-Labelling. Preprints.","key":"ref_48","DOI":"10.20944\/preprints201908.0073.v1"},{"unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013, January 2\u20134). Efficient Estimation of Word Representations in Vector Space. Proceedings of the ICLR Workshop, Scottsdale, AZ, USA.","key":"ref_49"},{"doi-asserted-by":"crossref","unstructured":"Ye, Z., Geng, Y., Chen, J., Chen, J., Xu, X., Zheng, S., Wang, F., Zhang, J., and Chen, H. (2020, January 5\u201310). Zero-Shot Text Classification via Reinforced Self-Training. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","key":"ref_50","DOI":"10.18653\/v1\/2020.acl-main.272"},{"unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA.","key":"ref_51"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3068335","article-title":"DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN","volume":"42","author":"Schubert","year":"2017","journal-title":"ACM Trans. Database Syst."},{"doi-asserted-by":"crossref","unstructured":"Pelicon, A., Pranji\u0107, M., Miljkovi\u0107, D., \u0160krlj, B., and Pollak, S. (2020). Zero-Shot Learning for Cross-Lingual News Sentiment Classification. Appl. Sci., 10.","key":"ref_53","DOI":"10.3390\/app10175993"},{"doi-asserted-by":"crossref","unstructured":"Mansar, Y., Gatti, L., Ferradans, S., Guerini, M., and Staiano, J. (2017, January 3\u20134). Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, BC, Canada.","key":"ref_54","DOI":"10.18653\/v1\/S17-2138"},{"doi-asserted-by":"crossref","unstructured":"Xiong, G., and Yan, K. (2021, January 25\u201328). Multi-Task Sentiment Classification Model Based on DistilBert and Multi-Scale CNN. Proceedings of the 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech), Calgary, AB, Canada.","key":"ref_55","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech52372.2021.00117"},{"doi-asserted-by":"crossref","unstructured":"Kicken, K., De Maesschalck, T., Vanrumste, B., De Keyser, T., and Shim, H.R. (2020, January 19). Intelligent Analyses on Storytelling for Impact Measurement. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), Online.","key":"ref_56","DOI":"10.18653\/v1\/2020.wnut-1.13"},{"key":"ref_57","first-page":"69","article-title":"Creativity, Requirements and Perspectives","volume":"13","author":"Hoffmann","year":"2005","journal-title":"Australas. J. Inf. Syst."},{"key":"ref_58","first-page":"297","article-title":"Unexpected Discoveries and S-Invention of Design Requirements: A Key to Creative Designs","volume":"21","author":"Suwa","year":"2006","journal-title":"Des. Stud."},{"unstructured":"Reimers, N. (2023, August 08). Pretrained Models Pretrained Models. Available online: https:\/\/www.sbert.net\/docs\/pretrained_models.html.","key":"ref_59"},{"doi-asserted-by":"crossref","unstructured":"Yin, W., Hay, J., and Roth, D. (2019, January 7). Benchmarking Zero-Shot Text Classification: Datasets, Evaluation and Entailment Approach. Proceedings of the EMNLP-IJCNLP 2019\u20142019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Hong Kong, China.","key":"ref_60","DOI":"10.18653\/v1\/D19-1404"},{"doi-asserted-by":"crossref","unstructured":"Tesfagergish, S.G., Kapo\u010di\u016bt\u0117-Dzikien\u0117, J., and Dama\u0161evi\u010dius, R. (2022). Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning. Appl. Sci., 12.","key":"ref_61","DOI":"10.3390\/app12178662"},{"doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2020, January 5\u201310). BART: Denoising Sequence-to-Sequence Pre-Training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","key":"ref_62","DOI":"10.18653\/v1\/2020.acl-main.703"},{"unstructured":"Grootendorst, M. (2022). BERTopic: Neural Topic Modeling with a Class-Based TF-IDF Procedure. arXiv.","key":"ref_63"},{"unstructured":"(2023, August 08). Manuel Bronstein Bringing You the Next-Generation Google Assistant. Available online: https:\/\/blog.google\/products\/assistant\/next-generation-google-assistant-io\/.","key":"ref_64"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/9\/403\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:41:12Z","timestamp":1760128872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/9\/403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,23]]},"references-count":64,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["a16090403"],"URL":"https:\/\/doi.org\/10.3390\/a16090403","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2023,8,23]]}}}