{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:59:47Z","timestamp":1750309187402,"version":"3.41.0"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["62102187 and 62372243"],"award-info":[{"award-number":["62102187 and 62372243"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004608","name":"Jiangsu Natural Science Foundation","doi-asserted-by":"crossref","award":["BK20210639"],"award-info":[{"award-number":["BK20210639"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2021YFE0104400"],"award-info":[{"award-number":["2021YFE0104400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Research Center of the Female Scientific and Medical Colleges, Deanship of Scientific Research, King Saud University, Saudi Arabia"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>\n            Online shopping has become a crucial way to encourage daily consumption, where the User-generated, or crowdsourced product comments, can offer a broad range of feedback on e-commerce products. As a result, integrating critical opinions or major attitudes from the crowdsourced comments can provide valuable feedback for marketing strategy adjustment or product-quality monitoring. Unfortunately, the scarcity of annotated ground truth on the integrated comment, or the limited gold integration reference, has incurred the infeasibility of the regular supervised-learning-based comment integration. To resolve this problem, in this article, inspired by the principle of Transfer Learning, we propose a three-stage transferable and generative crowdsourced comment integration framework (\n            <jats:bold>\n              <jats:italic>TTGCIF<\/jats:italic>\n            <\/jats:bold>\n            ) based on zero-and-few-shot learning with the support of domain distribution alignment. The proposed framework aims at generating abstractive integrated comment in target domain via the enhanced neural text generation model, by referring the available integration resource in related source domains, to avoid the exhausted effort on resource annotation devoted to the target domain. Specifically, at the first stage, to enhance the domain transferability, representations on the crowdsourced comments have been aligned up between the source and target domain, by minimizing the domain distribution discrepancy in the kernel space. At the second stage, Zero-shot comment integration mechanism has been adopted to deal with the dilemma that\n            <jats:bold>\n              <jats:italic>none<\/jats:italic>\n            <\/jats:bold>\n            of the gold integration reference may be available in target domain. In other words, taking the sample-level semantic prototype as input, the enhanced neural text generation model in\n            <jats:bold>\n              <jats:italic>TTGCIF<\/jats:italic>\n            <\/jats:bold>\n            is trained to learn data semantic association among different domains via semantic prototype transduction, so that the \u201c\n            <jats:bold>\n              <jats:italic>unlabeled<\/jats:italic>\n            <\/jats:bold>\n            \u201d crowdsourced comments in target domain can be associated with existing integration references in related source domains. At the third stage, based on the parameters trained at the second stage, fast domain adaptation mechanism in a Few-shot manner has also been adopted by seeking most potential parameters along the gradient direction constrained by instances across multiple source domains. In this way, parameters in\n            <jats:bold>\n              <jats:italic>TTGCIF<\/jats:italic>\n            <\/jats:bold>\n            can be sensitive to any alteration on training data, ensuring that even if only\n            <jats:bold>\n              <jats:italic>few<\/jats:italic>\n            <\/jats:bold>\n            annotated resource in target domain are available for \u201cFine-tune,\u201d\n            <jats:bold>\n              <jats:italic>TTGCIF<\/jats:italic>\n            <\/jats:bold>\n            can still react promptly to achieve effective target domain adaptation. According to the experimental results,\n            <jats:bold>\n              <jats:italic>TTGCIF<\/jats:italic>\n            <\/jats:bold>\n            can achieve the best transferable product comment integration performance in target domain, with fast and stable domain adaption effect depending on\n            <jats:bold>\n              <jats:italic>no more than<\/jats:italic>\n            <\/jats:bold>\n            10% annotated resource in target domain. More importantly, even if\n            <jats:bold>\n              <jats:italic>TTGCIF<\/jats:italic>\n            <\/jats:bold>\n            has not been fine-tuned on the target domain, yet by referring to the available integration resource in related source domains, the integrated comments generated by\n            <jats:bold>\n              <jats:italic>TTGCIF<\/jats:italic>\n            <\/jats:bold>\n            on the target domain are still superior to those generated by models already fine-tuned on the target domain.\n          <\/jats:p>","DOI":"10.1145\/3636511","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T11:25:23Z","timestamp":1702293923000},"page":"1-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Three-stage Transferable and Generative Crowdsourced Comment Integration Framework Based on Zero- and Few-shot Learning with Domain Distribution Alignment"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0542-1827","authenticated-orcid":false,"given":"Huan","family":"Rong","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science &amp; Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3911-3320","authenticated-orcid":false,"given":"Xin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science &amp; Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2320-1692","authenticated-orcid":false,"given":"Tinghuai","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science &amp; Technology, China and School of Computer Engineering, Jiangsu Ocean University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4960-174X","authenticated-orcid":false,"given":"Victor S.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Texas Tech University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7839-4933","authenticated-orcid":false,"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Auburn University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9790-5345","authenticated-orcid":false,"given":"Al-Rodhaan","family":"Mznah","sequence":"additional","affiliation":[{"name":"King Saud University, Kingdom of Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","volume-title":"Proceedings of the 7th International Conference on Learning Representations","author":"Antoniou Antreas","year":"2019","unstructured":"Antreas Antoniou, Harrison Edwards, and Amos Storkey. 2019. How to train your MAML. In Proceedings of the 7th International Conference on Learning Representations. 1\u201311."},{"key":"e_1_3_2_3_2","first-page":"6220\u2013 6231","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Cao Yue","year":"2020","unstructured":"Yue Cao, Hui Liu, and Xiaojun Wan. 2020. Jointly learning to align and summarize for neural cross-lingual summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6220\u2013 6231."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143865"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2964790"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2022.3230539"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.102"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2017.2708558"},{"key":"e_1_3_2_9_2","first-page":"4171","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1. 4171\u20134186."},{"key":"e_1_3_2_10_2","first-page":"0","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops","author":"Dornadula Apoorva","year":"2019","unstructured":"Apoorva Dornadula, Austin Narcomey, Ranjay Krishna, Michael Bernstein, and Fei-Fei Li. 2019. Visual relationships as functions: Enabling few-shot scene graph prediction. In Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops. 0\u20130."},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.18653\/v1\/P19-1305","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Duan Xiangyu","year":"2019","unstructured":"Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, and Weihua Luo. 2019. Zero-shot cross-lingual abstractive sentence summarization through teaching generation and attention. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 3162\u20133172."},{"key":"e_1_3_2_12_2","first-page":"704","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Fabbri Alexander R.","year":"2021","unstructured":"Alexander R. Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, and Yashar Mehdad. 2021. Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 704\u2013717."},{"key":"e_1_3_2_13_2","first-page":"1126","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1126\u20131135."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-016-9475-9"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1608"},{"key":"e_1_3_2_16_2","first-page":"2790","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Houlsby Neil","year":"2019","unstructured":"Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In Proceedings of the International Conference on Machine Learning. PMLR, 2790\u20132799."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-013-0306-1"},{"issue":"7","key":"e_1_3_2_18_2","first-page":"5872","article-title":"Assessment of data suitability for machine prognosis using maximum mean discrepancy","volume":"65","author":"Jia Xiaodong","year":"2017","unstructured":"Xiaodong Jia, Ming Zhao, Yuan Di, Qibo Yang, and Jay Lee. 2017. Assessment of data suitability for machine prognosis using maximum mean discrepancy. IEEE Trans. Industr. Electr. 65, 7 (2017), 5872\u20135881.","journal-title":"IEEE Trans. Industr. Electr."},{"key":"e_1_3_2_19_2","first-page":"675","volume-title":"Proceedings of the SIAM International Conference on Data Mining","author":"Keneshloo Yaser","year":"2019","unstructured":"Yaser Keneshloo, Naren Ramakrishnan, and Chandan K. Reddy. 2019. Deep transfer reinforcement learning for text summarization. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 675\u2013683."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11596"},{"issue":"3","key":"e_1_3_2_22_2","first-page":"984","article-title":"Heterogeneous domain adaptation via nonlinear matrix factorization","volume":"31","author":"Li Haoliang","year":"2019","unstructured":"Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot. 2019. Heterogeneous domain adaptation via nonlinear matrix factorization. IEEE Trans. Neural Netw. Learn. Syst. 31, 3 (2019), 984\u2013996.","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12141"},{"key":"e_1_3_2_24_2","first-page":"3730","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing","author":"Liu Yang","year":"2019","unstructured":"Yang Liu and Mirella Lapata. 2019. Text summarization with pretrained encoders. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 3730\u20133740."},{"key":"e_1_3_2_25_2","first-page":"1297","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing","author":"Liu Zihan","year":"2019","unstructured":"Zihan Liu, Jamin Shin, Yan Xu, Genta Indra Winata, Peng Xu, Andrea Madotto, and Pascale Fung. 2019. Zero-shot cross-lingual dialogue systems with transferable latent variables. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 1297\u20131303."},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Tinghuai Ma Huan Rong Yongsheng Hao Jie Cao Yuan Tian and Mznah A. Al-Rodhaan. 2019. A novel sentiment polarity detection framework for Chinese. IEEE Trans. Affect. Comput. 13 1 (2019) 60\u201374.","DOI":"10.1109\/TAFFC.2019.2932061"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0016-0032(96)00063-4"},{"key":"e_1_3_2_28_2","article-title":"On first-order meta-learning algorithms","author":"Nichol Alex","year":"2018","unstructured":"Alex Nichol, Joshua Achiam, and John Schulman. 2018. On first-order meta-learning algorithms. Retrieved from https:\/\/arXiv:1803.02999","journal-title":"Retrieved from https:\/\/arXiv:1803.02999"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.18653\/v1\/P19-1253","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Qian Kun","year":"2019","unstructured":"Kun Qian and Zhou Yu. 2019. Domain adaptive dialog generation via meta learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2639\u20132649."},{"key":"e_1_3_2_30_2","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel Colin","year":"2019","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21 (2019), 1\u201367.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.03.023"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Huan Rong Victor S. Sheng Tinghuai Ma Yang Zhou and Mznah A. Al-Rodhaan. 2020. A self-play and sentimentemphasized comment integration framework based on deep q-learning in a crowdsourcing scenario. IEEE Trans. Knowl. Data Eng. 34 3 (2020) 1021\u20131037.","DOI":"10.1109\/TKDE.2020.2993272"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1044"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2021.114135"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1099"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2018.2842432"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019837"},{"issue":"7","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/TKDE.2017.2659740","article-title":"Majority voting and pairing with multiple noisy labeling","volume":"31","author":"Sheng Victor S.","year":"2017","unstructured":"Victor S. Sheng, Jing Zhang, Bin Gu, and Xindong Wu. 2017. Majority voting and pairing with multiple noisy labeling. IEEE Trans. Knowl. Data Eng. 31, 7 (2017), 1355\u20131368.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_2_39_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Adv. Neural Info. Process. Syst. 30 (2017).","journal-title":"Adv. Neural Info. Process. Syst."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3464426"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105683"},{"issue":"2","key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3293318","article-title":"A survey of zero-shot learning: Settings, methods, and applications","volume":"10","author":"Wang Wei","year":"2019","unstructured":"Wei Wang, Vincent W. Zheng, Han Yu, and Chunyan Miao. 2019. A survey of zero-shot learning: Settings, methods, and applications. ACM Trans. Intell. Syst. Technol. 10, 2 (2019), 1\u201337.","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2864732"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269273"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.01.025"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107370"},{"issue":"5","key":"e_1_3_2_48_2","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/TCYB.2014.2344674","article-title":"Active learning with imbalanced multiple noisy labeling","volume":"45","author":"Zhang Jing","year":"2014","unstructured":"Jing Zhang, Xindong Wu, and Victor S. Shengs. 2014. Active learning with imbalanced multiple noisy labeling. IEEE Trans. Cybernet. 45, 5 (2014), 1095\u20131107.","journal-title":"IEEE Trans. Cybernet."},{"key":"e_1_3_2_49_2","first-page":"11328","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhang Jingqing","year":"2020","unstructured":"Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In Proceedings of the International Conference on Machine Learning. PMLR, 11328\u201311339."},{"key":"e_1_3_2_50_2","first-page":"1","volume-title":"Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue","author":"Zhao Tiancheng","year":"2018","unstructured":"Tiancheng Zhao and Maxine Eskenazi. 2018. Zero-shot dialog generation with cross-domain latent actions. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue. 1\u201310."},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.14778\/3055540.3055547"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636511","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3636511","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:54:11Z","timestamp":1750287251000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,4,30]]}},"alternative-id":["10.1145\/3636511"],"URL":"https:\/\/doi.org\/10.1145\/3636511","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2024,1,12]]},"assertion":[{"value":"2022-05-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-29","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}