{"id":8672,"date":"2026-06-27T14:34:28","date_gmt":"2026-06-27T06:34:28","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8672"},"modified":"2026-06-27T16:31:23","modified_gmt":"2026-06-27T08:31:23","slug":"jase-202610-33-020","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-020","title":{"rendered":"AI-Assisted Automated Micro-film Script Generation and Analysis System"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Dongxue Tan<\/i><\/a><\/p>\n\n\n\n

Lecturer, School of Journalism and Communication Shenyang Normal University, Shenyang 110000, China<\/p>\n<\/div>\n\n\n\n

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Received: February 26, 2026
Accepted: April 17, 2026
Publication Date: June 27, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Proposed Workflow for GPT-2\u2013Based Micro-Film Script Generation.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.020<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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With the rise of short stories in the digital medium, there has been a growth of interest in developing intelligent, scalable systems that generate micro-film scripts. This paper introduces an Artificial Intelligence-assisted automated framework to generate micro-film scripts, and to analyze their narrative structure and content using a Fine-tuned GPT-2 Transformer Model. Movie scripts are pre-processed to normalize, tokenize by sub-words, and segment the text into fixed-length sequences to develop “screenplay” specific narrative patterns. The model is then further trained using the AdamW Optimiser with a learning rate of 5E-5, a batch size of 8 and training over 3 epochs; additionally, micro-film scripts will be generated using Nucleus Sampling (top-p = 0.9), and Temperature Scaling (temperature = 0.8). An extensive evaluation framework has been used to evaluate the outputs of the proposed framework via BLEU, ROUGE, Distinct-N, Semantic Similarity, Narrative Coherence, and Sentiment Consistency metrics. The experimental evaluations showed a proposed framework generated high semantic similarity (0.93), strong lexical diversity (Dist-2 = 0.83), and stable sentiment consistency (0.555) indicating a proposed framework generated fluent, creative scripts. However, global narrative coherence was evidenced to be only moderate (0.264); therefore, the proposed framework could provide a reproducible and scalable solution to allow for automated micro-film script creation and use in creative, educational and commercial industries.<\/p>\n\n\n\n

Keywords: Micro-films; Short dramas; documentaries; And other film and television genres; AI-Assisted Script Generation<\/em><\/p>\n\n\n\n

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