To unfold the tremendous amount of audiovisual data uploaded daily to social media platforms, effective topic modelling techniques are needed. Existing work tends to apply variants of topic models on text data sets. In this paper, we aim at developing a topic extractor on video transcriptions. The model improves coherence by exploiting neural word embeddings through a graph-based clustering method. Unlike typical topic models, this approach works without knowing the true number of topics. Experimental results on the real-life multimodal data set MuSe-CaR demonstrates that our approach extracts coherent and meaningful topics, outperforming baseline methods. Furthermore, we successfully demonstrate the generalisability of our approach on a pure text review data set.

中文翻译：

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视频转录中基于无监督图的主题建模

为了展现每天上传到社交媒体平台的海量视听数据，需要有效的主题建模技术。现有工作倾向于将主题模型的变体应用于文本数据集。在本文中，我们旨在开发有关视频转录的主题提取器。该模型通过基于图的聚类方法利用神经词嵌入来提高一致性。与典型的主题模型不同，此方法在不知道真实主题数的情况下起作用。在现实生活中的多峰数据集MuSe-CaR上的实验结果表明，我们的方法提取了连贯且有意义的主题，胜过了基线方法。此外，我们成功地证明了我们的方法在纯文本审阅数据集上的一般性。