Stories are a very compelling medium to convey ideas, experiences, social and
cultural values. Narrative is a specific manifestation of the story that turns
it into knowledge for the audience. In this paper, we propose a machine
learning approach to capture the narrative elements in movies by bridging the
gap between the low-level data representations and semantic aspects of the
visual medium. We present a Memory-Augmented Video Semantic Network, called
Video SemNet, to encode the semantic descriptors and learn an embedding for the
video. The model employs two main components: (i) a neural semantic learner
that learns latent embeddings of semantic descriptors and (ii) a memory module
that retains and memorizes specific semantic patterns from the video. We
evaluate the video representations obtained from variants of our model on two
tasks: (a) genre prediction and (b) IMDB Rating prediction. We demonstrate that
our model is able to predict genres and IMDB ratings with a weighted F-1 score
of 0.72 and 0.63 respectively. The results are indicative of the
representational power of our model and the ability of such representations to
measure audience engagement.

该研究通过提出一种机器学习方法，介绍了一种记忆增强视频语义网络来捕捉电影中的叙事元素，从而在两个任务中成功预测电影的流派和 IMDB 评分。