{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T03:10:06Z","timestamp":1758856206497,"version":"3.44.0"},"reference-count":40,"publisher":"Wiley","issue":"23-24","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2025,10,25]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>In recent years, many studies have focused on unsupervised data\u2010to\u2010text generation methods. However, existing unsupervised methods still require a large amount of unlabeled sample training, leading to significant data collection overhead. We propose a low\u2010resource unsupervised method called CycleRUR. This method first converts various forms of structured data (such as tables, knowledge graph(KG) triples, and meaning representations(MR)) into unified KG triples to improve the model's ability to adapt to different structured data. Additionally, CycleRUR incorporates a retraining module and a contrastive learning module within a cycle training framework, enabling the model to learn and converge from a small amount of unpaired KG triples and reference text corpus, thereby improving the model's accuracy and convergence speed. We evaluated the model's performance on the WebNLG and E2E datasets. Using only 10% of unpaired training data, our method achieved the effects of fully supervised fine\u2010tuning. On the WebNLG dataset, it resulted in an 18.41% improvement in METEOR compared to supervised models. On the E2E dataset, it achieved improvements of 1.37% in METEOR and 4.97% in BLEU. Experiments also demonstrated that under unified linearization, CycleRUR exhibits good generalization capabilities.<\/jats:p>","DOI":"10.1002\/cpe.70254","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:52:36Z","timestamp":1756947156000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Promoting Unsupervised Data\u2010To\u2010Text Generation Using Retraining and Unified Linearization"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0996-5070","authenticated-orcid":false,"given":"Xiaobo","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software Yunnan University  Yunnan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2929-2126","authenticated-orcid":false,"given":"Xuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software Yunnan University  Yunnan China"},{"name":"Yunnan Key Laboratory of Software Engineering Yunnan University  Yunnan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Cheng","sequence":"additional","affiliation":[{"name":"Smart City Business Group Yunnan Nantian Electronics Information Corp. Ltd.  Yunnan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6462-4598","authenticated-orcid":false,"given":"Kunpeng","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering Yunnan University  Yunnan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering Yunnan University  Yunnan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuxian","family":"Ma","sequence":"additional","affiliation":[{"name":"Smart City Business Group Yunnan Nantian Electronics Information Corp. Ltd.  Yunnan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Smart City Business Group Yunnan Nantian Electronics Information Corp. Ltd.  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