On the effects of using speech transcripts and subtitles to detect topic shifts in news broadcasts
Summary
In this research, topic segmentation in texts (a.k.a. text segmentation) is used as a proxy for
topic segmentation in videos. The main application is automatically providing a topic transition
structure for videos, because it is difficult to quickly scan them and figure out where a new
subject starts. Topic models are used to figure out the topic transition positions. The available
data for this research is provided by the Netherlands Institute for Sound and Vision and consists
of 25,600 transcripts and subtitles of the same Dutch news broadcasts.
The research questions whether it is better to use automatic speech recognition transcripts or
subtitles when segmenting a video based on topics.The subtitles and speech transcripts were
compared for the same news broadcasts and both qualitative and quantitative differences
between them were found. However, no significant difference was found between the
performance of the text segmentation algorithm using subtitles and speech transcripts. The
research presents the challenges and benefits of the developed text segmentation algorithm.
The research can give insight into the realizability of the application of text segmentation to help
structure videos, which can become a starting point for future research.