{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T14:06:30Z","timestamp":1769522790765,"version":"3.49.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the National Social Science Foundation of China","award":["17ATQ001"],"award-info":[{"award-number":["17ATQ001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01675-x","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T09:46:47Z","timestamp":1734601607000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["cLegal-QA: a Chinese legal question answering with natural language generation methods"],"prefix":"10.1007","volume":"11","author":[{"given":"Yizhen","family":"Wang","sequence":"first","affiliation":[]},{"given":"Xueying","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Zixian","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Lihui","family":"Niu","sequence":"additional","affiliation":[]},{"given":"Shiyan","family":"Ou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"issue":"5","key":"1675_CR1","first-page":"21","volume":"5","author":"AK Aggarwal","year":"2015","unstructured":"Aggarwal AK (2015) On the use of artificial intelligence techniques in transportation systems. Int J Soft Comput Eng 5(5):21\u201324","journal-title":"Int J Soft Comput Eng"},{"issue":"4","key":"1675_CR2","first-page":"77","volume":"6","author":"K Arora","year":"2017","unstructured":"Arora K, Kumar A (2017) A comparative study on content based image retrieval methods. Int J Technol Eng Manag Appl Sci 6(4):77\u201380","journal-title":"Int J Technol Eng Manag Appl Sci"},{"key":"1675_CR3","doi-asserted-by":"crossref","unstructured":"Bach NX, Thien THN, Phuong TM (2017) Question analysis for Vietnamese legal question answering. In: 2017 9th international conference on knowledge and systems engineering (KSE), Hue","DOI":"10.1109\/KSE.2017.8119451"},{"issue":"4","key":"1675_CR4","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1017\/S1351324907004664","volume":"14","author":"A Belz","year":"2008","unstructured":"Belz A (2008) Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models. Nat Lang Eng 14(4):431\u2013455. https:\/\/doi.org\/10.1017\/S1351324907004664","journal-title":"Nat Lang Eng"},{"key":"1675_CR5","unstructured":"Caballero EQ, Rahman MS, Cerny T, Rivas P, Bejarano G (2022) Study of question answering on legal software document using BERT based models. In: LatinX in natural language processing research workshop"},{"key":"1675_CR6","unstructured":"Cao Y, Li S, Liu Y, Yan Z, Dai Y, Yu PS, Sun L (2023) A comprehensive survey of ai-generated content (aigc): a history of generative ai from gan to chatgpt. arXiv:2303.04226"},{"key":"1675_CR7","doi-asserted-by":"crossref","unstructured":"Corbelle, J. G., Diz, A. B., Alonso-Moral, J., Taboada, J. (2022). Dealing with hallucination and omission in neural Natural Language Generation: A use case on meteorology. Proceedings of the 15th International Conference on Natural Language Generation, Maine, USA.","DOI":"10.18653\/v1\/2022.inlg-main.10"},{"key":"1675_CR8","doi-asserted-by":"crossref","unstructured":"Das R, Ray A, Mondal S, Das D (2016) A rule based question generation framework to deal with simple and complex sentences. In: 2016 international conference on advances in computing, communications and informatics (ICACCI), Jaipur","DOI":"10.1109\/ICACCI.2016.7732102"},{"key":"1675_CR9","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"key":"1675_CR10","unstructured":"Dong L, Yang N, Wang W, Wei F, Liu X, Wang Y, Gao J, Zhou M, Hon H-W (2019) Unified language model pre-training for natural language understanding and generation. In: Advances in neural information processing systems, p 32"},{"key":"1675_CR11","doi-asserted-by":"crossref","unstructured":"Du Z, Qian Y, Liu X, Ding M, Qiu J, Yang Z, Tang J (2021) Glm: general language model pretraining with autoregressive blank infilling. arXiv:2103.10360","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"1675_CR12","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s00354-019-00070-2","volume":"37","author":"B Fawei","year":"2019","unstructured":"Fawei B, Pan JZ, Kollingbaum M, Wyner AZ (2019) A semi-automated ontology construction for legal question answering. New Gener Comput 37:453\u2013478. https:\/\/doi.org\/10.1007\/s00354-019-00070-2","journal-title":"New Gener Comput"},{"issue":"3","key":"1675_CR13","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1609\/aimag.v31i3.2303","volume":"31","author":"D Ferrucci","year":"2010","unstructured":"Ferrucci D, Brown E, Chu-Carroll J, Fan J, Gondek D, Kalyanpur AA, Lally A, Murdock JW, Nyberg E, Prager J (2010) Building Watson: an overview of the DeepQA project. AI Mag 31(3):59\u201379. https:\/\/doi.org\/10.1609\/aimag.v31i3.2303","journal-title":"AI Mag"},{"key":"1675_CR14","doi-asserted-by":"crossref","unstructured":"Fu Y, Feng Y (2018) Natural answer generation with heterogeneous memory. In: Proceedings of the 2018 conference of the North American Chapter of the association for computational linguistics: human language technologies, vol 1 (Long Papers), New Orleans","DOI":"10.18653\/v1\/N18-1017"},{"key":"1675_CR15","unstructured":"Kano Y, Hoshino R, Taniguchi R (2017) Analyzable legal yes\/no question answering system using linguistic structures. COLIEE@ ICAIL"},{"issue":"7","key":"1675_CR16","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1587\/transinf.2018EDP7199","volume":"102","author":"O Keklik","year":"2019","unstructured":"Keklik O, Tuglular T, Tekir S (2019) Rule-based automatic question generation using semantic role labeling. IEICE Trans Inf Syst 102(7):1362\u20131373. https:\/\/doi.org\/10.1587\/transinf.2018EDP7199","journal-title":"IEICE Trans Inf Syst"},{"key":"1675_CR17","doi-asserted-by":"crossref","unstructured":"Khazaeli S, Punuru J, Morris C, Sharma S, Staub B, Cole M, Chiu-Webster S, Sakalley D (2021) A free format legal question answering system. In: Proceedings of the natural legal language processing workshop 2021, Punta Cana","DOI":"10.18653\/v1\/2021.nllp-1.11"},{"key":"1675_CR18","doi-asserted-by":"crossref","unstructured":"Khullar P, Rachna K, Hase M, Shrivastava M (2018) Automatic question generation using relative pronouns and adverbs. In: Proceedings of ACL 2018, student research workshop, Melbourne","DOI":"10.18653\/v1\/P18-3022"},{"key":"1675_CR19","doi-asserted-by":"crossref","unstructured":"Kien PM, Nguyen H-T, Bach NX, Tran V, Le Nguyen M, Phuong TM (2020) Answering legal questions by learning neural attentive text representation. In: Proceedings of the 28th international conference on computational linguistics, Barcelona (Online)","DOI":"10.18653\/v1\/2020.coling-main.86"},{"key":"1675_CR20","unstructured":"Kim M-Y, Lu Y, Rabelo J, Goebel R (2018) Coliee-2018: evaluation of the competition on case law information extraction and entailment. In: Proceedings of the twelfth international workshop on juris-informatics (JURISIN 2018)"},{"key":"1675_CR21","doi-asserted-by":"crossref","unstructured":"Kim M-Y, Xu Y, Goebel R (2017) Applying a convolutional neural network to legal question answering. In: New frontiers in artificial intelligence: JSAI-isAI 2015 Workshops, LENLS, JURISIN, AAA, HAT-MASH, TSDAA, ASD-HR, and SKL, Kanagawa, November 16\u201318, 2015, Revised Selected Papers","DOI":"10.1007\/978-3-319-50953-2_20"},{"issue":"24","key":"1675_CR22","doi-asserted-by":"publisher","first-page":"5412","DOI":"10.1016\/j.ins.2011.07.047","volume":"181","author":"O Kolomiyets","year":"2011","unstructured":"Kolomiyets O, Moens M-F (2011) A survey on question answering technology from an information retrieval perspective. Inf Sci 181(24):5412\u20135434. https:\/\/doi.org\/10.1016\/j.ins.2011.07.047","journal-title":"Inf Sci"},{"key":"1675_CR23","doi-asserted-by":"crossref","unstructured":"Kourtin I, Mbarki S, Mouloudi A (2021) A legal question answering ontology-based system. In: Formalising natural languages: applications to natural language processing and digital humanities: 14th international conference, NooJ 2020, Zagreb, June 5\u20137, 2020, Revised Selected Papers 14","DOI":"10.1007\/978-3-030-70629-6_19"},{"key":"1675_CR24","unstructured":"Kumar A, Oishi T, Ono S, Banno A, Ikeuchi K (2013) Global coordinate adjustment of 3D survey models in world geodetic system under unstable GPS condition. In: 20th ITS World CongressITS Japan"},{"key":"1675_CR25","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.30534\/ijatcse\/2020\/36922020","volume":"9","author":"T Kumari","year":"2020","unstructured":"Kumari T, Syal P, Aggarwal AK, Guleria V (2020) Hybrid image registration methods: a review. Int J Adv Trends Comput Sci Eng 9:1134\u20131142","journal-title":"Int J Adv Trends Comput Sci Eng"},{"key":"1675_CR26","doi-asserted-by":"crossref","unstructured":"Lelkes AD, Tran VQ, Yu C (2021) Quiz-style question generation for news stories. In: Proceedings of the web conference 2021","DOI":"10.1145\/3442381.3449892"},{"key":"1675_CR27","doi-asserted-by":"crossref","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2019) Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv:1910.13461","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"1675_CR28","unstructured":"Li X, Hu S, Zou L (2020) Natural answer generation via graph transformer. In: Web and big data: 4th international joint conference, APWeb-WAIM 2020, Tianjin, September 18\u201320, 2020, Proceedings, Part I 4"},{"key":"1675_CR29","unstructured":"Lin C-Y (2004) Rouge: a package for automatic evaluation of summaries. In: Text summarization branches out, Barcelona"},{"key":"1675_CR30","doi-asserted-by":"crossref","unstructured":"Linh LH, Long NH, Yen NH (2021) Vietnamese legal question answering with combined features and deep learning. In: 2021 13th international conference on knowledge and systems engineering (KSE), Bangkok","DOI":"10.1109\/KSE53942.2021.9648797"},{"key":"1675_CR31","doi-asserted-by":"crossref","unstructured":"Martinez-Gil J, Freudenthaler B, Tjoa AM (2019) Multiple choice question answering in the legal domain using reinforced co-occurrence. In: Database and expert systems applications: 30th international conference, DEXA 2019, Linz, August 26\u201329, 2019, Proceedings, Part I 30","DOI":"10.1007\/978-3-030-27615-7_10"},{"issue":"2","key":"1675_CR32","first-page":"1","volume":"4","author":"M Mohamed","year":"2023","unstructured":"Mohamed M (2023) Agricultural sustainability in the age of deep learning: current trends, challenges, and future trajectories. Sustain Mach Intell J 4(2):1\u201320","journal-title":"Sustain Mach Intell J"},{"issue":"5","key":"1675_CR33","first-page":"1","volume":"3","author":"M Mohamed","year":"2023","unstructured":"Mohamed M (2023) Empowering deep learning based organizational decision making: a survey. Sustain Mach Intell J 3(5):1\u201313","journal-title":"Sustain Mach Intell J"},{"issue":"12","key":"1675_CR34","doi-asserted-by":"publisher","first-page":"6070","DOI":"10.48550\/arXiv.2306.06494","volume":"26","author":"JH Moon","year":"2022","unstructured":"Moon JH, Lee H, Shin W, Kim Y-H, Choi E (2022) Multi-modal understanding and generation for medical images and text via vision-language pre-training. IEEE J Biomed Health Inform 26(12):6070\u20136080. https:\/\/doi.org\/10.48550\/arXiv.2306.06494","journal-title":"IEEE J Biomed Health Inform"},{"key":"1675_CR35","doi-asserted-by":"publisher","unstructured":"Morimoto A, Kubo D, Sato M, Shindo H, Matsumoto Y (2017) Legal question answering system using neural attention. COLIEE@ ICAIL, pp 79\u201389. https:\/\/doi.org\/10.29007\/4l2q","DOI":"10.29007\/4l2q"},{"key":"1675_CR36","doi-asserted-by":"crossref","unstructured":"M\u00fcller P, Kaissis G, Zou C, Rueckert D (2022) Joint learning of localized representations from medical images and reports. In: European conference on computer vision","DOI":"10.1007\/978-3-031-19809-0_39"},{"key":"1675_CR37","doi-asserted-by":"crossref","unstructured":"Nguyen C, Bui M-Q, Do D-T, Le N-K, Nguyen D-H, Nguyen T-T, Nguyen H-T, Tran V, Nguyen L-M, Le N-C (2022) ALQAC 2022: A Summary of the competition. In: 2022 14th international conference on knowledge and systems engineering (KSE), Nha Trang","DOI":"10.1109\/KSE56063.2022.9953764"},{"key":"1675_CR38","unstructured":"OpenAI (2023) Introducing ChatGPT. https:\/\/openai.com\/blog\/chatgpt"},{"key":"1675_CR39","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics","DOI":"10.3115\/1073083.1073135"},{"key":"1675_CR40","doi-asserted-by":"crossref","unstructured":"Quaresma P, Rodrigues I (2005) A question-answering system for Portuguese juridical documents. In: Proceedings of the 10th international conference on artificial intelligence and law","DOI":"10.1145\/1165485.1165536"},{"issue":"1","key":"1675_CR41","doi-asserted-by":"publisher","first-page":"5485","DOI":"10.48550\/arXiv.1910.10683","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(1):5485\u20135551. https:\/\/doi.org\/10.48550\/arXiv.1910.10683","journal-title":"J Mach Learn Res"},{"key":"1675_CR42","doi-asserted-by":"crossref","unstructured":"Scialom T, Piwowarski B, Staiano J (2019) Self-attention architectures for answer-agnostic neural question generation. In: Proceedings of the 57th annual meeting of the association for computational linguistics, Florence","DOI":"10.18653\/v1\/P19-1604"},{"key":"1675_CR43","doi-asserted-by":"crossref","unstructured":"Sovrano F, Palmirani M, Distefano B, Sapienza S, Vitali F (2021) A dataset for evaluating legal question answering on private international law. In: Proceedings of the eighteenth international conference on artificial intelligence and law","DOI":"10.1145\/3462757.3466094"},{"key":"1675_CR44","doi-asserted-by":"publisher","unstructured":"Sovrano F, Palmirani M, Vitali F (2020) Legal knowledge extraction for knowledge graph based question-answering. In: Legal knowledge and information systems. IOS Press, pp 143\u2013153. https:\/\/doi.org\/10.3233\/FAIA200858","DOI":"10.3233\/FAIA200858"},{"key":"1675_CR45","doi-asserted-by":"crossref","unstructured":"Sun X, Liu J, Lyu Y, He W, Ma Y, Wang S (2018) Answer-focused and position-aware neural question generation. In: Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels","DOI":"10.18653\/v1\/D18-1427"},{"key":"1675_CR46","doi-asserted-by":"crossref","unstructured":"Taniguchi R, Hoshino R, Kano Y (2019) Legal question answering system using framenet. In: New frontiers in artificial intelligence: JSAI-isAI 2018 workshops, JURISIN, AI-Biz, SKL, LENLS, IDAA, Yokohama, November 12\u201314, 2018, Revised selected papers","DOI":"10.1007\/978-3-030-31605-1_15"},{"key":"1675_CR47","doi-asserted-by":"crossref","unstructured":"Tieu T-T, Chau C-N, Nguyen T-S, Nguyen L-M (2021) Apply Bert-based models and domain knowledge for automated legal question answering tasks at ALQAC 2021. In: 2021 13th international conference on knowledge and systems engineering (KSE), Bangkok","DOI":"10.1109\/KSE53942.2021.9648727"},{"key":"1675_CR48","doi-asserted-by":"publisher","unstructured":"Tosyal\u0131 H, Aytekin \u00c7 (2020) Development of robot journalism application: Tweets of news content in the Turkish language shared by a bot. J Inf Technol Manag 12(Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications):68\u201388. https:\/\/doi.org\/10.22059\/JITM.2020.79335","DOI":"10.22059\/JITM.2020.79335"},{"key":"1675_CR49","doi-asserted-by":"crossref","unstructured":"Van HN, Nguyen D, Nguyen PM, Le Nguyen M (2022) Miko team: deep learning approach for legal question answering in alqac 2022. In: 2022 14th international conference on knowledge and systems engineering (KSE)","DOI":"10.1109\/KSE56063.2022.9953780"},{"key":"1675_CR50","unstructured":"Veena G, Gupta D, Anil A, Akhil S (2019) An ontology driven question answering system for legal documents. In: 2019 2nd international conference on intelligent computing, instrumentation and control technologies (ICICICT), Kannur"},{"key":"1675_CR51","doi-asserted-by":"crossref","unstructured":"Wang C, Luo X (2021) A legal question answering system based on BERT. In: Proceedings of the 2021 5th international conference on computer science and artificial intelligence","DOI":"10.1145\/3507548.3507591"},{"key":"1675_CR52","doi-asserted-by":"publisher","first-page":"61008","DOI":"10.1109\/Access.2019.2904337","volume":"7","author":"MX Wei","year":"2019","unstructured":"Wei MX, Zhang Y (2019) Natural answer generation with attention over instances. IEEE Access 7:61008\u201361017. https:\/\/doi.org\/10.1109\/Access.2019.2904337","journal-title":"IEEE Access"},{"key":"1675_CR53","doi-asserted-by":"crossref","unstructured":"Wiratchawa K, Khunthong T, Intharah T (2021) LegalBERT-th: development of legal Q&A dataset and automatic question tagging. In: 2021 18th international conference on electrical engineering\/electronics, computer, telecommunications and information technology (ECTI-CON), Chiang Mai","DOI":"10.1109\/ECTI-CON51831.2021.9454753"},{"key":"1675_CR54","unstructured":"Wu J, Gan W, Chen Z, Wan S, Lin H (2023) Ai-generated content (aigc): a survey. arXiv:2304.06632"},{"key":"1675_CR55","unstructured":"Wyner AZ, Fawei BJ, Pan JZ (2016) Passing a USA national bar exam: a first corpus for experimentation. In: LREC 2016, tenth international conference on language resources and evaluation"},{"key":"1675_CR56","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.aiopen.2022.11.002","volume":"3","author":"H Zhang","year":"2022","unstructured":"Zhang H, Zhang T, Cao F, Wang Z, Zhang Y, Sun Y, Vicente MA (2022) BCA: bilinear convolutional neural networks and attention networks for legal question answering. AI Open 3:172\u2013181. https:\/\/doi.org\/10.1016\/j.aiopen.2022.11.002","journal-title":"AI Open"},{"key":"1675_CR57","doi-asserted-by":"crossref","unstructured":"Zhang W (2022) Application and development of robot sports news writing by artificial intelligence. In: 2022 IEEE 2nd international conference on data science and computer application (ICDSCA), Dalian","DOI":"10.1109\/ICDSCA56264.2022.9988077"},{"key":"1675_CR58","doi-asserted-by":"crossref","unstructured":"Zhong H, Xiao C, Tu C, Zhang T, Liu Z, Sun M (2020) JEC-QA: a legal-domain question answering dataset. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v34i05.6519"},{"key":"1675_CR59","unstructured":"Zhou Q, Yang N, Wei F, Tan C, Bao H, Zhou M (2018) Neural question generation from text: a preliminary study. In: Natural language processing and Chinese computing: 6th CCF international conference, NLPCC 2017, Dalian, November 8\u201312, 2017, Proceedings 6"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01675-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01675-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01675-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:21:23Z","timestamp":1738268483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01675-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1675"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01675-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"11 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have influenced influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The datasets employed in our study are publicly accessible and have been released by the respective organizations\/authors to advance research in the field of Chinese civil legal question answering. We have adhered to all ethical standards in utilizing these datasets. If you are interested in accessing the dataset associated with this article, we kindly request that you contact us via email to acquire a license for the data, ensuring its use aligns with ethical guidelines and the original purposes of the dataset.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}],"article-number":"77"}}