Due to recent advances in the film industry, the production of movies has grown exponentially, which has led to challenges in what is referred to as discoverability: given the overwhelming number of choices, choosing which film to watch has become a tedious task for audiences. Movie summarization (MS) could help, as it presents the central theme of the movie in a compact format and makes browsing more efficient for the audience. In this paper, we present an automatic MS framework coined as ‘QuickLook’, which identifies the leading characters and fuses multiple cues extracted from a movie. Firstly, the movie data is preprocessed for its division into scenes, followed by shot segmentation. Secondly, the leading characters in each segmented scene are determined. Next, four visual cues that capture the film's scenic beauty, memorability, informativeness and emotional resonance are extracted from shots containing the leading characters. These extracted features are then intelligently fused based on the assignment of different weights; shots with a fusion score above a certain threshold are selected for the final summary. The proposed MS framework is assessed by comparison with official trailers from ten Hollywood movies, providing a novel baseline for future fair comparison in the MS literature. The proposed framework is shown to outperform other state-of-the-art MS methods in terms of enjoyability and informativeness.

由于电影业的最新发展，电影的制作呈指数增长，这在所谓的可发现性方面带来了挑战：鉴于选择太多，选择观看哪部电影已成为观众的繁琐任务。电影摘要（MS）可能会有所帮助，因为它以紧凑的格式呈现了电影的中心主题，并使观众的浏览更加高效。在本文中，我们提出了一个自动创建的MS框架，称为“ QuickLook”，该框架可识别主角并融合从电影中提取的多个提示。首先，对电影数据进行预处理以将其划分为场景，然后进行镜头分割。其次，确定每个分割场景中的前导字符。接下来，通过四个视觉线索来捕捉影片的风景之美，令人难忘，信息和情感共鸣是从包含主角的镜头中提取的。然后根据不同权重的分配将这些提取的特征进行智能融合；选择融合分数高于某个阈值的镜头作为最终摘要。通过与十部好莱坞电影的官方预告片进行比较，对提议的MS框架进行了评估，从而为MS文献中的未来公平比较提供了新的基线。在娱乐性和信息性方面，所提出的框架显示出优于其他最新的MS方法。选择融合分数高于某个阈值的镜头作为最终摘要。通过与十部好莱坞电影的官方预告片进行比较，对提议的MS框架进行了评估，从而为MS文献中的未来公平比较提供了新的基线。在娱乐性和信息性方面，所提出的框架显示出优于其他最新的MS方法。选择融合分数高于某个阈值的镜头作为最终摘要。通过与十部好莱坞电影的官方预告片进行比较，对提议的MS框架进行了评估，从而为MS文献中的未来公平比较提供了新的基线。在娱乐性和信息性方面，所提出的框架显示出优于其他最新的MS方法。