{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T05:07:10Z","timestamp":1764392830605,"version":"3.46.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"DOI":"10.1007\/s11063-025-11764-8","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T03:52:15Z","timestamp":1763005935000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Paraphrase Generation Model Using Semantically and Syntactically Controlled Structures"],"prefix":"10.1007","volume":"57","author":[{"given":"Chandni","family":"Magoo","sequence":"first","affiliation":[]},{"given":"Manjeet","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"11764_CR1","unstructured":"Prakash A, Hasan SA, Lee K, Datla VV, Qadir A, Liu J, Farri O (2016) Neural paraphrase generation with stacked residual LSTM networks,pp.2923\u20132934. In: The 26th international conference on computational linguistics: technical papers"},{"key":"11764_CR2","doi-asserted-by":"crossref","unstructured":"Abisheka P, Deisy C, Sharmil P (2024) T-SRE: transformer-based semantic relation extraction for contextual paraphrased plagiarism detection. J King Saud Univ Comput Inf Sci, p 102257","DOI":"10.1016\/j.jksuci.2024.102257"},{"key":"11764_CR3","doi-asserted-by":"crossref","unstructured":"Wang A, Singh A, Michael J, Hill F, Levy O, Bowman S (2018)\u00a0GLUE: a multi-task benchmark and analysis platform for natural language understanding. In:\u00a0Proceedings of the 2018 EMNLP workshop blackbox NLP: analyzing and interpreting neural networks for NLP, Brussels, Belgium. Association for Computational Linguistics, pp 353\u2013355","DOI":"10.18653\/v1\/W18-5446"},{"issue":"4","key":"11764_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3446770","volume":"20","author":"M Alian","year":"2021","unstructured":"Alian M, Awajan A, Al-Hasan A, Akuzhia R (2021) Building Arabic paraphrasing benchmark based on transformation rules. Trans Asian Low Resource Lang Inf Process 20(4):1\u201317","journal-title":"Trans Asian Low Resource Lang Inf Process"},{"key":"11764_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-44999-5_38","author":"AK Kushwaha","year":"2020","unstructured":"Kushwaha AK, Kar AK, VigneswaraIlavarasan P (2020) Predicting information diffusion on twitter a deep learning neural network model using custom weighted word features. Respons Des Implement Use Inf Commun Technol. https:\/\/doi.org\/10.1007\/978-3-030-44999-5_38","journal-title":"Respons Des Implement Use Inf Commun Technol"},{"key":"11764_CR6","unstructured":"Chandni M, Manjeet S (2022) Comparative study and experimentation on different techniques for customized pos-tagging. Int J Intell Inf Database Syst (accepted, inprint)"},{"key":"11764_CR7","unstructured":"Lin C-Y (2004) Rouge: a package for automatic evaluation of summaries. Text summarization branches out, pp 74\u201381. Association for Computational Linguistics"},{"key":"11764_CR8","doi-asserted-by":"crossref","unstructured":"Gupta A, Agarwal A, Singh P, Rai P (2018) A deep generative framework for paraphrase generation. In:\u00a0Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11956"},{"key":"11764_CR9","doi-asserted-by":"publisher","unstructured":"Abonizio HQ, Paraiso EC, Barbon Junior S (2021) Toward text data augmentation for sentiment analysis. In: IEEE Trans Artif Intell (2021) https:\/\/doi.org\/10.1109\/TAI.2021.3114390","DOI":"10.1109\/TAI.2021.3114390"},{"key":"11764_CR10","doi-asserted-by":"crossref","unstructured":"Yang H, Lam W, Li P (2021) Contrastive representation learning for exemplar-guided paraphrase generation.\u00a0arXiv preprint arXiv:2109.01484","DOI":"10.18653\/v1\/2021.findings-emnlp.409"},{"key":"11764_CR11","doi-asserted-by":"crossref","unstructured":"Palivela H (2021) Optimization of paraphrase generation and identification using language models in natural language processing. Int J Inf Manage Data Insight","DOI":"10.1016\/j.jjimei.2021.100025"},{"key":"11764_CR12","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. Assoc Comput Linguist, pp 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"11764_CR13","doi-asserted-by":"publisher","unstructured":"Jain R, Kathuria A, Singh A, Saxena A, Khandelwal A (2021) ParaCap: paraphrase detection model using capsule network. In; AAAI conference on artificial intelligence multimedia systems, pp 1\u201319. https:\/\/doi.org\/10.1007\/S00530-020-00746-6","DOI":"10.1007\/S00530-020-00746-6"},{"key":"11764_CR14","doi-asserted-by":"publisher","unstructured":"Kathleen RM (1983) Paraphrasing questions using given and new information. Comput Linguist, pp1\u201310, https:\/\/doi.org\/10.1145\/965105.807463","DOI":"10.1145\/965105.807463"},{"key":"11764_CR15","doi-asserted-by":"publisher","unstructured":"Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. In:\u00a0Proceedings of the 40th annual meeting of the association for computational linguistics, pp 311\u2013318, https:\/\/doi.org\/10.3115\/1073083.1073135","DOI":"10.3115\/1073083.1073135"},{"key":"11764_CR16","doi-asserted-by":"crossref","unstructured":"Huang K-H, Chang K-W (2021) Generating syntactically controlled paraphrases without using annotated parallel pairs. arXiv preprint arXiv:2101.10579","DOI":"10.18653\/v1\/2021.eacl-main.88"},{"key":"11764_CR17","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.neunet.2022.01.016","volume":"148","author":"C Mi","year":"2022","unstructured":"Mi C, Xie L, Zhang Y (2022) Improving data augmentation for low resource speech-to-text translation with diverse paraphrasing. Neural Netw 148:194\u2013205","journal-title":"Neural Netw"},{"key":"11764_CR18","doi-asserted-by":"publisher","unstructured":"Chen M, Tang Q, Wiseman S, Gimpel K (2019b) Controllable paraphrase generation with a syntactic exemplar. In: Proceedings of the 57th annual meeting of the association for computational linguistics, Florence, Italy. pp 5972\u20135984, \u00a92019 Association for Computational Linguistics, https:\/\/doi.org\/10.18653\/v1\/P19-1599","DOI":"10.18653\/v1\/P19-1599"},{"key":"11764_CR19","doi-asserted-by":"publisher","unstructured":"Iyyer M, Wieting J, Gimpel K, Zettlemoyer L (2018) Adversarial example generation with syntactically controlled paraphrase networks. In: Proceedings of NAACL-HLT, pp 1875\u20131885. https:\/\/doi.org\/10.18653\/v1\/N18-1170","DOI":"10.18653\/v1\/N18-1170"},{"key":"11764_CR20","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamesebert-networks. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, Hong Kong, China, pp 3982\u20133992. \u00a9 2019 Association for Computational Linguistics","DOI":"10.18653\/v1\/D19-1410"},{"key":"11764_CR21","doi-asserted-by":"crossref","unstructured":"Ouahrani L, Bennouar D (2024) Paraphrase generation and supervised learning for improved automatic short answer grading. Int J Artif Intell Educ, pp 1\u201344","DOI":"10.1007\/s40593-023-00391-w"},{"key":"11764_CR22","unstructured":"Rehurek R, Sojka P (2011) Gensim\u2013python framework for vector space modelling. NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic 3.2"},{"key":"11764_CR23","unstructured":"Colin R, 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"},{"key":"11764_CR24","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1162\/COLI_a_00166","volume":"39","author":"R Bhagat","year":"2013","unstructured":"Bhagat R, Hovy E (2013) What is a paraphrase? Comput Linguist 39:463\u2013472. https:\/\/doi.org\/10.1162\/COLI_a_00166","journal-title":"Comput Linguist"},{"key":"11764_CR25","doi-asserted-by":"publisher","unstructured":"Honeck RP (1971) A study of paraphrases.\u00a0J Verb Learn Verbal Behav, pp367\u2013381, https:\/\/doi.org\/10.1016\/S0022-5371(71)80035-X","DOI":"10.1016\/S0022-5371(71)80035-X"},{"key":"11764_CR26","unstructured":"Banerjee S, Lavie A (2005) METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In:\u00a0Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and\/or summarization, pp 65\u201372. Association for Computational Linguistics"},{"key":"11764_CR27","unstructured":"Iyer S, andekar N, Csernai K (2017) First quora dataset release: Question pairs.\u00a0data. quora. com"},{"key":"11764_CR28","doi-asserted-by":"crossref","unstructured":"Goyal T, Durrett G (2020) Neural syntactic preordering for controlled paraphrase generation. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 238\u2013252, 2020-Association for Computational Linguistics","DOI":"10.18653\/v1\/2020.acl-main.22"},{"key":"11764_CR29","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In:\u00a0Adv Neural Inf Process Syst, pp 3111\u20133119"},{"key":"11764_CR30","doi-asserted-by":"publisher","unstructured":"Kajiwara T (2019) Negative lexically constrained decoding for paraphrase generation. In:\u00a0Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 6047\u20136052. https:\/\/doi.org\/10.18653\/v1\/P19-1607","DOI":"10.18653\/v1\/P19-1607"},{"key":"11764_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113679","author":"WS El-Kassas","year":"2021","unstructured":"El-Kassas WS, Salama CR, Rafea AA, Mohamed HK (2021) Automatic text summarization: a comprehensive survey. Exp Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2020.113679","journal-title":"Exp Syst Appl"},{"key":"11764_CR32","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.neucom.2022.02.020","volume":"485","author":"E Yang","year":"2022","unstructured":"Yang E, Liu M, Xiong D, Zhang Y, Meng Y, Xu J, Chen Y (2022) Improving generation diversity via syntax-controlled paraphrasing. Neurocomputing 485:103\u2013113","journal-title":"Neurocomputing"},{"key":"11764_CR33","unstructured":"Zhang J, Zhao Y, Saleh M, Liu P (2020) Pegasus: pre-training with extracted gap-sentences for abstractive summarization. In:\u00a0International conference on machine learning. PMLR"},{"key":"11764_CR34","doi-asserted-by":"crossref","unstructured":"Nguyen HT, Duong PH, Cambria E (2019) Learning short-text semantic similarity with word embeddings and external knowledge sources. Knowl Based Syst 182: 104842","DOI":"10.1016\/j.knosys.2019.07.013"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-025-11764-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-025-11764-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-025-11764-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T05:02:34Z","timestamp":1764392554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-025-11764-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["11764"],"URL":"https:\/\/doi.org\/10.1007\/s11063-025-11764-8","relation":{},"ISSN":["1573-773X"],"issn-type":[{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2025,11,13]]},"assertion":[{"value":"5 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2025","order":2,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"94"}}