{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T18:06:58Z","timestamp":1779214018494,"version":"3.51.4"},"reference-count":103,"publisher":"Informa UK Limited","issue":"2","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Smart Growth, Digitization and Financial Instruments Program","award":["334906"],"award-info":[{"award-number":["334906"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information and Telecommunication"],"published-print":{"date-parts":[[2026,4,3]]},"DOI":"10.1080\/24751839.2025.2587991","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T11:47:26Z","timestamp":1763552846000},"page":"330-359","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["From text to idea embeddings: a review on quintessential information extraction"],"prefix":"10.1080","volume":"10","author":[{"given":"Robert","family":"Chihaia","sequence":"first","affiliation":[{"name":"\u201cGheorghe Asachi\u201d Technical University of Ia\u015fi","place":["Ia\u015fi, Romania"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1370-9145","authenticated-orcid":false,"given":"Florin","family":"Leon","sequence":"additional","affiliation":[{"name":"\u201cGheorghe Asachi\u201d Technical University of Ia\u015fi","place":["Ia\u015fi, Romania"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6241-0126","authenticated-orcid":false,"given":"Maria","family":"Trocan","sequence":"additional","affiliation":[{"name":"Institut Sup\u00e9rieur d'\u00c9lectronique de Paris (ISEP)","place":["Paris, France"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"301","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"The Claude 3 Model Family: Opus Sonnet Haiku. https:\/\/api.semanticscholar.org\/ CorpusID:268232499."},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2022.102131"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494560"},{"key":"e_1_3_3_5_1","unstructured":"Achiam J. Adler S. Agarwal S. Ahmad L. Akkaya I. Aleman F. L. Almeida D. Altenschmidt J. Altman S. Anadkat S. Avila R. Babuschkin I. Balaji S. Balcom V. Baltescu P. Bao H. Bavarian M. Belgum J. Bello I. & Zoph B. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774."},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-eacl.161"},{"key":"e_1_3_3_8_1","doi-asserted-by":"crossref","unstructured":"Bano S. & Khalid S. (2022). Bert-based extractive text summarization of scholarly articles: A novel architecture. In 2022 International Conference on Artificial Intelligence of Things (ICAIoT) (pp. 1\u20135). IEEE.","DOI":"10.1109\/ICAIoT57170.2022.10121826"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1907375117"},{"key":"e_1_3_3_10_1","unstructured":"Behrouz A. Zhong P. & Mirrokni V. (2025). Titans: Learning to memorize at test time. arXiv preprint arXiv:2501.00663."},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17489"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/521"},{"key":"e_1_3_3_13_1","unstructured":"Br\u00fcel-Gabrielsson R. Nelson B. J. Dwaraknath A. Skraba P. Guibas L. J. & Carlsson G. (2019). A topology layer for machine learning. arXiv preprint arXiv:1905.12200. https:\/\/proceedings.mlr.press\/v108\/gabrielsson20a.html."},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW58026.2022.00060"},{"issue":"2","key":"e_1_3_3_15_1","first-page":"1761","article-title":"Learning relation prototype from unlabeled texts for long-tail relation extraction","volume":"35","author":"Cao Y.","year":"2021","unstructured":"Cao, Y., Kuang, J., Gao, M., Zhou, A., Wen, Y., & Chua, T.-S. (2021). Learning relation prototype from unlabeled texts for long-tail relation extraction. IEEE Transactions on Knowledge and Data Engineering, 35(2), 1761\u20131774.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.48448\/KRTT-GS17"},{"key":"e_1_3_3_17_1","doi-asserted-by":"crossref","unstructured":"Chen X. Ghoshal A. Mehdad Y. Zettlemoyer L. & Gupta S. (2020). Low-resource domain adaptation for compositional task-oriented semantic parsing. arXiv preprint arXiv:2010.03546.","DOI":"10.18653\/v1\/2020.emnlp-main.413"},{"key":"e_1_3_3_18_1","doi-asserted-by":"crossref","unstructured":"Chihaia R. Trocan M. & Leon F. (2025). Simple idea discovery in a minimalist llm architecture implementation. In Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS 2025) Krakow Poland.","DOI":"10.15439\/2025F5480"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3150409"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.506"},{"key":"e_1_3_3_22_1","unstructured":"Devlin J. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805."},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00325"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3249783"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3452720"},{"key":"e_1_3_3_26_1","unstructured":"Drozdov A. Sch\u00e4rli N. Aky\u00fcrek E. Scales N. Song X. Chen X. Bousquet O. & Zhou D. (2023). Compositional semantic parsing with large language models. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=gJW8hSGBys8"},{"key":"e_1_3_3_27_1","unstructured":"Dubey A. Jauhri A. Pandey A. Kadian A. Al-Dahle A. Letman A. Mathur A. Schelten A. Yang A. Fan A. Goyal A. Hartshorn A. Yang A. Mitra A. Sravankumar A. Korenev A. Hinsvark A. Rao A. Zhang A. & Ma Z. (2024). The llama 3 herd of models. arXiv preprint arXiv:2407.21783."},{"key":"e_1_3_3_28_1","unstructured":"Elhage N. Hume T. Olsson C. Schiefer N. Henighan T. Kravec S. Hatfield-Dodds Z. Lasenby R. Drain D. Chen C. Grosse R. McCandlish S. Kaplan J. Amodei D. Wattenberg M. & Olah C. (2022). Toy models of superposition. arXiv preprint arXiv:2209.10652."},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118842"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.06.089"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17514"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17515"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.6570655"},{"key":"e_1_3_3_34_1","doi-asserted-by":"crossref","unstructured":"Gao T. Yao X. & Chen D. (2021). SimCSE: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821. https:\/\/aclanthology.org\/2021.emnlp-main.552\/.","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"e_1_3_3_35_1","unstructured":"Gemini Team Google (2024). Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530."},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"e_1_3_3_37_1","unstructured":"Grootendorst M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794."},{"key":"e_1_3_3_38_1","unstructured":"Guo D. Yang D. Zhang H. Song J. Zhang R. Xu R. Zhu Q. Ma S. Wang P. Bi X. Zhang X. Yu X. Wu Y. Wu Z. F. Gou Z. Shao Z. Li Z. Gao Z. Liu A. & Zhang Z. (2025). DeepSeek-R1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948."},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"key":"e_1_3_3_40_1","doi-asserted-by":"crossref","unstructured":"Herzig J. & Berant J. (2020). Span-based semantic parsing for compositional generalization. arXiv preprint arXiv:2009.06040.","DOI":"10.18653\/v1\/2021.acl-long.74"},{"key":"e_1_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"key":"e_1_3_3_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01087-6"},{"key":"e_1_3_3_43_1","doi-asserted-by":"crossref","unstructured":"Jiang C. Maddela M. Lan W. Zhong Y. & Xu W. (2020). Neural CRF model for sentence alignment in text simplification. https:\/\/aclanthology.org\/2020.acl-main.709\/","DOI":"10.18653\/v1\/2020.acl-main.709"},{"key":"e_1_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02764938"},{"key":"e_1_3_3_45_1","unstructured":"Kaplan J. McCandlish S. Henighan T. Brown T. B. Chess B. Child R. Gray S. Radford A. Wu J. & Amodei D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361."},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.967"},{"key":"e_1_3_3_47_1","unstructured":"Krizhevsky A. Sutskever I. & Hinton G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (Vol. 25). Association for Computing Machinery. https:\/\/dl.acm.org\/doi\/10.1145\/3065386."},{"key":"e_1_3_3_48_1","unstructured":"Laskin M. Wang L. Oh J. Parisotto E. Spencer S. Steigerwald R. Strouse D. Hansen S. Filos A. Brooks E. Gazeau M. Sahni H. Singh S. & Mnih V. (2022). In-context reinforcement learning with algorithm distillation. arXiv preprint arXiv:2210.14215. https:\/\/neurips.cc\/virtual\/2022\/66293."},{"key":"e_1_3_3_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-70259-4_19"},{"key":"e_1_3_3_50_1","unstructured":"Leon F. (2024b). A review of findings from neuroscience and cognitive psychology as possible inspiration for the path to artificial general intelligence (pp. 1\u2013143). arXiv preprint arXiv:2401.10904."},{"key":"e_1_3_3_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981314"},{"key":"e_1_3_3_52_1","unstructured":"Li J. Wang J. Zhang Z. & Zhao H. (2022). Self-prompting large language models for zero-shot open-domain QA. arXiv preprint arXiv:2212.08635."},{"key":"e_1_3_3_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3213168"},{"key":"e_1_3_3_54_1","doi-asserted-by":"crossref","unstructured":"Lin S. Xie H. Wang B. Yu K. Chang X. Liang X. & Wang G. (2022). Knowledge distillation via the target-aware transformer. arXiv preprint arXiv:2205.10793. https:\/\/ieeexplore.ieee.org\/document\/9879889.","DOI":"10.1109\/CVPR52688.2022.01064"},{"key":"e_1_3_3_55_1","doi-asserted-by":"crossref","unstructured":"Lin X. V. Socher R. & Xiong C. (2020). Bridging textual and tabular data for cross-domain text-to-SQL semantic parsing. arXiv preprint arXiv:2012.12627.","DOI":"10.18653\/v1\/2020.findings-emnlp.438"},{"key":"e_1_3_3_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"key":"e_1_3_3_57_1","doi-asserted-by":"crossref","unstructured":"Luo Z. (2023). Knowledge-guided aspect-based summarization. In 2023 International conference on communications computing and artificial intelligence (CCCAI) (pp. 17\u201322). IEEE.","DOI":"10.1109\/CCCAI59026.2023.00012"},{"key":"e_1_3_3_58_1","doi-asserted-by":"crossref","unstructured":"Maddela M. Alva-Manchego F. & Xu W. (2020). Controllable text simplification with explicit paraphrasing. arXiv preprint arXiv:2010.11004. https:\/\/aclanthology.org\/2021.naacl-main.277\/.","DOI":"10.18653\/v1\/2021.naacl-main.277"},{"key":"e_1_3_3_59_1","doi-asserted-by":"crossref","unstructured":"Majeed M. & Kala M. (2023). Comparative study on extractive summarization using sentence ranking algorithm and text ranking algorithm. In 2023 International conference on power instrumentation control and computing (PICC) (pp. 1\u20135). IEEE.","DOI":"10.1109\/PICC57976.2023.10142314"},{"key":"e_1_3_3_60_1","unstructured":"Mikolov T. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781."},{"key":"e_1_3_3_61_1","unstructured":"Mirzadeh I. Alizadeh K. Shahrokhi H. Tuzel O. Bengio S. & Farajtabar M. (2024). Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models. arXiv preprint arXiv:2410.05229."},{"key":"e_1_3_3_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2019.05.012"},{"key":"e_1_3_3_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.6221020"},{"key":"e_1_3_3_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2024.3458796"},{"key":"e_1_3_3_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.6570655"},{"key":"e_1_3_3_66_1","unstructured":"Nickel M. & Kiela D. (2017). Poincar\u00e9 embeddings for learning hierarchical representations. In I. Guyon U. V. Luxburg S. Bengio H. Wallach R. Fergus S. Vishwanathan & R. Garnett (Eds.) Advances in Neural Information Processing Systems (Vol. 30). Curran Associates Inc. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/59dfa2df42d9e3d41f5b02bfc32229dd-Paper.pdf"},{"key":"e_1_3_3_67_1","unstructured":"Nie S. Zhu F. You Z. Zhang X. Ou J. Hu J. Zhou J. Lin Y. Wen J.-R. & Li C. (2025). Large language diffusion models. arXiv preprint arXiv:2502.09992."},{"key":"e_1_3_3_68_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-022-01481-7"},{"key":"e_1_3_3_69_1","unstructured":"Omelianchuk K. Raheja V. & Skurzhanskyi O. (2021). Text simplification by tagging. arXiv preprint arXiv:2103.05070. https:\/\/aclanthology.org\/2021.bea-1.2\/."},{"key":"e_1_3_3_70_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocac149"},{"key":"e_1_3_3_71_1","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang L.","year":"2022","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730\u201327744.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_72_1","first-page":"606","article-title":"Efficiently scaling transformer inference","volume":"5","author":"Pope R.","year":"2023","unstructured":"Pope, R., Douglas, S., Chowdhery, A., Devlin, J., Bradbury, J., Heek, J., Xiao, K., Agrawal, S., & Dean, J. (2023). Efficiently scaling transformer inference. Proceedings of Machine Learning and Systems, 5, 606\u2013624.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_3_73_1","doi-asserted-by":"crossref","unstructured":"Prabhakar P. Gupta D. & Pati P. B. (2022). Abstractive summarization of indian legal judgments. In 2022 OITS International Conference on Information Technology (OCIT) (pp. 256\u2013261). IEEE.","DOI":"10.1109\/OCIT56763.2022.00056"},{"key":"e_1_3_3_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2992485"},{"key":"e_1_3_3_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.6570650"},{"key":"e_1_3_3_76_1","unstructured":"Radford A. (2018). Improving language understanding by generative pre-training. https:\/\/cdn.openai.com\/research-covers\/language-unsupervised\/language_understanding_paper.pdf."},{"key":"e_1_3_3_77_1","doi-asserted-by":"crossref","unstructured":"Rajpurkar P. Zhang J. Lopyrev K. & Liang P. (2016). Squad: 100 000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250.","DOI":"10.18653\/v1\/D16-1264"},{"key":"e_1_3_3_78_1","doi-asserted-by":"crossref","unstructured":"Ramani K. Bhavana K. Akshaya A. Harshita K. S. Kumar C. T. & Srikanth M. (2023). An explorative study on extractive text summarization through k-means LSA and textrank. In 2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) (pp. 1\u20136). IEEE.","DOI":"10.1109\/WiSPNET57748.2023.10134303"},{"key":"e_1_3_3_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380064"},{"key":"e_1_3_3_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084125"},{"key":"e_1_3_3_81_1","unstructured":"Shleifer S. & Rush A. M. (2020). Pre-trained summarization distillation. arXiv preprint arXiv:2010.13002."},{"key":"e_1_3_3_82_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-eacl.161"},{"key":"e_1_3_3_83_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"e_1_3_3_84_1","doi-asserted-by":"crossref","unstructured":"Stajner S. (2021). Automatic text simplification for social good: Progress and challenges. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 2637\u20132652). Association for Computational Linguistics. https:\/\/aclanthology.org\/W13-2322\/","DOI":"10.18653\/v1\/2021.findings-acl.233"},{"key":"e_1_3_3_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/ijcnn52387.2021.9533769"},{"issue":"1","key":"e_1_3_3_86_1","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani A.","year":"2017","unstructured":"Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30(1), 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_87_1","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei J.","year":"2022","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824\u201324837.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_88_1","unstructured":"Wu X. Dong X. Nguyen T. T. & Luu A. T. (2023). Effective neural topic modeling with embedding clustering regularization. In International Conference on Machine Learning (pp. 37335\u201337357). PMLR."},{"key":"e_1_3_3_89_1","doi-asserted-by":"crossref","unstructured":"Wu X. Luu A. T. & Dong X. (2022). Mitigating data sparsity for short text topic modeling by topic-semantic contrastive learning. arXiv preprint arXiv:2211.12878.","DOI":"10.18653\/v1\/2022.emnlp-main.176"},{"key":"e_1_3_3_90_1","unstructured":"Yang K. Klein D. Celikyilmaz A. Peng N. & Tian Y. (2023). Rlcd: Reinforcement learning from contrastive distillation for language model alignment. arXiv preprint arXiv:2307.12950."},{"key":"e_1_3_3_91_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108488"},{"key":"e_1_3_3_92_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa189"},{"key":"e_1_3_3_93_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171"},{"key":"e_1_3_3_94_1","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"key":"e_1_3_3_95_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.5962385"},{"key":"e_1_3_3_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.3039681"},{"key":"e_1_3_3_97_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3053563"},{"key":"e_1_3_3_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"key":"e_1_3_3_99_1","unstructured":"Zhang T. Kishore V. Wu F. Weinberger K. Q. & Artzi Y. (2019). Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675. https:\/\/openreview.net\/pdf?id=SkeHuCVFDr."},{"key":"e_1_3_3_100_1","doi-asserted-by":"publisher","DOI":"10.1109\/Access.6287639"},{"key":"e_1_3_3_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3159366"},{"key":"e_1_3_3_102_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3380415"},{"key":"e_1_3_3_103_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2022.3153261"},{"key":"e_1_3_3_104_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210080"}],"container-title":["Journal of Information and Telecommunication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/24751839.2025.2587991","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T17:17:08Z","timestamp":1779211028000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/24751839.2025.2587991"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,19]]},"references-count":103,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4,3]]}},"alternative-id":["10.1080\/24751839.2025.2587991"],"URL":"https:\/\/doi.org\/10.1080\/24751839.2025.2587991","relation":{},"ISSN":["2475-1839","2475-1847"],"issn-type":[{"value":"2475-1839","type":"print"},{"value":"2475-1847","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,19]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tjit20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tjit20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2025-03-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}