Identifying Keyword Predictors in Lecture Video Screen Text

Automatic discovery of keywords for lecture video segments is an important component of advanced navigation systems for lecture videos. The suitability of a word or a short phrase to be a keyword depends on various factors, including the frequency in a segment, relative frequency in reference to the full video, font size, time on screen, and the existence in domain and language dictionaries. The research presented in this paper provides a refined understanding of how various factors contribute to predicting keywords based on logistic regression analysis. The analysis employs a real-world dataset consisting of lecture videos from Biology, Computer Science, and Chemistry, hosted on Videopoints, a lecture video management portal. Term frequency, maximum font size, and presence in a domain dictionary were identified as the most important predictors of keywords. The results provide a scientific foundation and valuable insights into the design of future keyword prediction systems.