{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:18:37Z","timestamp":1777130317886,"version":"3.51.4"},"reference-count":16,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Digit. Gov.: Res. Pract."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>This application paper introduces a transformative solution to address the labour-intensive manual report generation, data searching and report revision process in government entities. Traditional methods of data extraction, analysis, and graph creation for annual reports are not only time-consuming but also prone to human errors. To mitigate these challenges, we propose an innovative system leveraging Generative Artificial Intelligence (GenAI), with a specific focus on large language models (LLMs). Our solution incorporates automated data extraction from diverse sources designated as internal knowledge base, text analysis, summarization using advanced language models, and the generation as well as revisions of informative graphs. Different LLMs like Google's Gemini Pro and OpenAI's GPT 4.0 have been used to read different data visualisation graphs and fetch the information from the internal knowledge base, respectively, to update the graphs in an automated manner. Solution implementation using Python language on the World Economic Situation and Prospects report by the Department of Economic &amp; Social Affairs, United Nation shows that the early result produces almost 0.87% to 17.46% average error rates in the task of factual data visualisation graph. Key benefits of this approach include improved time efficiency, consistency in report format, enhanced insights, and a user-friendly interface. A review and approval workflow facilitate user feedback, contributing to continuous model performance improvement.<\/jats:p>","DOI":"10.1145\/3691352","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T10:54:02Z","timestamp":1725274442000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Automating Government Report Generation: A Generative AI Approach for Efficient Data Extraction, Analysis, and Visualization"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9851-4047","authenticated-orcid":false,"given":"Rajan","family":"Gupta","sequence":"first","affiliation":[{"name":"Artificial Intelligence &amp; Innovation (AI&amp;I) Lab, Autonomous University of Tamaulipas, Ciudad Victoria, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0075-1739","authenticated-orcid":false,"given":"Gaurav","family":"Pandey","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, IIT Jodhpur, Jodhpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3297-1605","authenticated-orcid":false,"given":"Saibal Kumar","family":"Pal","sequence":"additional","affiliation":[{"name":"SAG Lab, Defence Research and Development Organisation, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Challenges in using data across government","author":"CAG.","year":"2019","unstructured":"CAG. 2019. Challenges in using data across government. National Audit Office. Report accessed from https:\/\/www.nao.org.uk\/wp-content\/uploads\/2019\/06\/Challenges-in-using-data-across-government.pdf on 10th January 2024.","journal-title":"National Audit Office"},{"issue":"1","key":"e_1_3_1_3_2","first-page":"917","article-title":"Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline","volume":"26","author":"Chen Z.","year":"2019","unstructured":"Z. Chen, Y. Wang, Q. Wang, Y. Wang, and H. Qu. 2019. Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline. IEEE Transactions on Visualization and Computer Graphics 26, 1 (2019), 917\u2013926.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techsoc.2023.102372"},{"key":"e_1_3_1_5_2","unstructured":"G. Team R. Anil S. Borgeaud Y. Wu J. B. Alayrac J. Yu and J. Ahn. 2023. Gemini: A family of highly capable multimodal models. arXiv preprint arXiv:2312.11805."},{"key":"e_1_3_1_6_2","unstructured":"J. Ye X. Chen N. Xu C. Zu Z. Shao S. Liu and X. Huang. 2023. A comprehensive capability analysis of GPT-3 and GPT-3.5 series models. arXiv preprint arXiv:2303.10420."},{"key":"e_1_3_1_7_2","unstructured":"M. Boubdir E. Kim B. Ermis S. Hooker and M. Fadaee. 2023. Elo uncovered: Robustness and best practices in language model evaluation. arXiv preprint arXiv:2311.17295."},{"key":"e_1_3_1_8_2","unstructured":"J. Chen H. Lin X. Han and L. Sun. 2023. Benchmarking large language models in retrieval-augmented generation. arXiv preprint arXiv:2309.01431."},{"key":"e_1_3_1_9_2","unstructured":"United Nations. 2022. World Economic Situation and Prospects Report Department of Economic and Social Affairs report accessed from https:\/\/www.un.org\/development\/desa\/dpad\/publication\/world-economic-situation-and-prospects-2022\/#:~:text=%22In%20this%20fragile%20and%20uneven gaps%20within%20and%20among%20countries [PDF: https:\/\/desapublications.un.org\/file\/728\/download] on 15th Jan 2024."},{"key":"e_1_3_1_10_2","unstructured":"United Nations. 2024. World Economic Situation and Prospects Report Department of Economic and Social Affairs Report accessed from https:\/\/www.un.org\/development\/desa\/dpad\/publication\/world-economic-situation-and-prospects-2024\/ [PDF: https:\/\/www.un.org\/development\/desa\/dpad\/wp-content\/uploads\/sites\/45\/WESP_2024_Web.pdf] on 15th Jan 2024."},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/jrsm.1481"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2016.01.002"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/jrsm.1232"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2009.04.007"},{"key":"e_1_3_1_15_2","first-page":"3","article-title":"The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews","author":"Cramond F.","year":"2018","unstructured":"F. Cramond, A. O'Mara-Eves, L. Doran-Constant, A. S. Rice, M. Macleod, and J. Thomas. 2018. The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews. Wellcome Open Research 3.","journal-title":"Wellcome Open Research"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.5812\/ijem.95216"},{"key":"e_1_3_1_17_2","first-page":"1917","article-title":"ChartOCR: Data extraction from charts images via a deep hybrid framework","author":"Luo J.","year":"2021","unstructured":"J. Luo, Z. Li, J. Wang, and C. Y. Lin. 2021. ChartOCR: Data extraction from charts images via a deep hybrid framework. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 1917\u20131925.","journal-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision"}],"container-title":["Digital Government: Research and Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3691352","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3691352","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:49:57Z","timestamp":1750268997000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3691352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,12]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3,31]]}},"alternative-id":["10.1145\/3691352"],"URL":"https:\/\/doi.org\/10.1145\/3691352","relation":{},"ISSN":["2691-199X","2639-0175"],"issn-type":[{"value":"2691-199X","type":"print"},{"value":"2639-0175","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,12]]},"assertion":[{"value":"2024-01-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}