Automatic Identification of Keywords in Lecture Video Segments

Lecture video is an increasingly important learning resource. However, the challenge of quickly finding the content of interest in a long lecture video is a critical limitation of this format. This paper introduces automatic discovery of keywords (or tags) for lecture video segments to improve navigation. A lecture video is divided into topical segments based on the frame-to-frame similarity of content. A user navigates the lecture video assisted by visual summaries and keywords for the segments. Keywords provide an overview of the content discussed in the segment to improve navigation. The input to the keyword identification algorithm is the text from the video frames extracted by OCR. Automatically discovering keywords is challenging as the suitability of an N-gram to be a keyword depends on a variety of factors including frequency in a segment and relative frequency in reference to the full video, font size, time on screen, and the existence in domain and language dictionaries. This paper explores how these factors are quantified and combined to identify good keywords. The key scientific contribution of this paper is the design, implementation, and evaluation of a keyword selection algorithm for lecture video segments. Evaluation is performed by comparing the keywords generated by the algorithm with the tags chosen by experts on 121 segments of 11 videos from STEM courses.