LVTIA: A new method for keyphrase extraction from scientific video lectures

Due to the growth of technology, the expansion of communication infrastructure and crises of COVID-19 pandemic, e-learning and virtual education is expanding. One of the best ways to access and organize these information is indexing using automatic intelligent methods. Indexing requires assigning keywords or keyphrases to each video, to represent its content. The main focus of this research is to propose an approach by which appropriate keyphrases are assigned to scientific video lectures. For this purpose, a new algorithm called LVTIA, Lecture Video Text mining-base Indexing Algorithm, is proposed in which the textual content of video frames along with the text extracted from audio signal are merged together, and a new keyphrase extraction method is proposed. The proposed method considers new local and global features for each candidate phrases, along with a new feature reflecting the occurrence of each phrase in the audio signals or video frames. The method is implemented using five distinct data sets in English and Persian.