{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T01:03:12Z","timestamp":1752282192933,"version":"3.40.5"},"reference-count":89,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,10,21]]},"abstract":"<jats:p>With the development of modern science and technology, information technology has brought great changes to many fields. Smart justice has become one of the increasing areas that people are paying more attention to. For example, large and small cases occur every day, and the legal library is continuously updated. Therefore, a large number of documents and evidence collection archives will bring tremendous pressure on the judiciary. The text generation technology can automatically present the results extracted from these redundant legal data and express the results of the analysis in natural language. It facilitates the business for huge amounts of legal data effectively, which relieves the work pressure of the judicial department. However, the text generation algorithms have not been promoted in justice. Therefore, this paper focuses on what benefits text generation can produce in law and how to apply text generation technology in legal field. The survey provides a comprehensive overview on text generation firstly, through summarizing the existing methods, that is, text to text, data to text, and visual to text. Then, we examine the process of the practical application of text generation in law. Furthermore, this paper puts forward the challenges and possible solutions to the judicial text generation, which provides pointers on future work.<\/jats:p>","DOI":"10.1155\/2021\/3225933","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T16:27:32Z","timestamp":1634833652000},"page":"1-14","source":"Crossref","is-referenced-by-count":2,"title":["Exploration of Cross-Modal Text Generation Methods in Smart Justice"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5085-0949","authenticated-orcid":true,"given":"Yangqianhui","family":"Zhang","sequence":"first","affiliation":[{"name":"The University of British Columbia, School of Biomedical Engineering, Vancouver, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.1017\/s0269888999004026"},{"doi-asserted-by":"publisher","key":"2","DOI":"10.3115\/974557.974596"},{"doi-asserted-by":"publisher","key":"3","DOI":"10.1145\/371920.372071"},{"doi-asserted-by":"publisher","key":"4","DOI":"10.1145\/138859.138867"},{"doi-asserted-by":"publisher","key":"5","DOI":"10.1145\/3322640.3326740"},{"issue":"1","key":"6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/MSP.2010.939038","article-title":"Deep learning and its applications to signal and information processing [exploratory dsp]","volume":"28","author":"D. 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