用文本嵌入预测奥斯卡最佳剧本提名，效果优于随机基准。
Predicting Oscar-Nominated Screenplays with Sentence Embeddings
- 用E5模型对剧本、摘要、标题编码，融合三类特征进行预测。
- 最优模型宏F1达0.66，ROC-AUC为0.79，显著高于基线。
- 适合对影视工业影响力分析或剧本评估感兴趣的读者。
奥斯卡提名对电影行业具有重要影响，能提升作品的曝光度与商业价值。本文探索利用现代语言模型预测奥斯卡最佳剧本提名的可能性。由于缺乏合适数据集，研究构建了名为Movie-O-Label的新数据集，将电影剧本集合MovieSum与经人工筛选的奥斯卡获奖记录相结合。每部剧本以标题、维基百科摘要和完整剧本表示，长剧本被切分为重叠文本块，并使用E5句子嵌入模型编码。通过逻辑回归模型对剧本嵌入进行分类，当融合剧本、摘要和标题三类特征输入时表现最佳。最先进模型达到宏F1分数0.66，精确率-召回率平均精度（PR-AUC）为0.445（基线0.19），ROC-AUC为0.79。结果表明，基于现代文本嵌入的简单模型已具备良好预测能力，可为后续研究提供起点。
原文摘要 · Abstract (English)
Oscar nominations are an important factor in the movie industry because they can boost both the visibility and the commercial success. This work explores whether it is possible to predict Oscar nominations for screenplays using modern language models. Since no suitable dataset was available, a new one called Movie-O-Label was created by combining the MovieSum collection of movie scripts with curated Oscar records. Each screenplay was represented by its title, Wikipedia summary, and full script. Long scripts were split into overlapping text chunks and encoded with the E5 sentence em bedding model. Then, the screenplay embed dings were classified using a logistic regression model. The best results were achieved when three feature inputs related to screenplays (script, summary, and title) were combined. The best-performing model reached a macro F1 score of 0.66, a precision recall AP of 0.445 with baseline 0.19 and a ROC-AUC of 0.79. The results suggest that even simple models based on modern text embeddings demonstrate good prediction performance and might be a starting point for future research.
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