Automatic Identification of Keywords in Lecture Video Segments

Koka, Raga Shalini; Chowdhury, Farah Naz; Rahman, Mohammad Rajiur; Solorio, Thamar; Subhlok, Jaspal

doi:10.1109/ism.2020.00035

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…A very similar study to ours was found in the works of Raga et al They used this method to navigate to the exact part of the video, or access a video segment by just searching for the keyword. They considered factors like statistical and visual features while implementing the algorithm [19]. A model called Text Rank was developed by Rada and Paul where they took a graphical approach to rank the words [20].…”

Section: Related Workmentioning

confidence: 99%

Extract Concept using Subtitles in MOOC

Kawtar¹,

Benlahmar²,

Naji³

et al. 2022

IJACSA

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Massive open online courses (MOOCs) are a variety of courses offered through the online mode, paid or unpaid and has evolved as an excellent learning resource for students. The structure of the course design is mainly linear where there are a few video lectures provided by either professors of several universities, or people with expertise in the particular subject. They are usually graded on a weekly basis through quizzes or peer-graded assignments. The objective of this paper is to extract the concepts taught in the videos from the subtitles, which could later be used to enhance recommendations of the learners using their clickstream data. The teachers could also use this to see the demand for their courses. Evaluate two keyword extraction methods, which are BERT and LDA using different Coursera courses. The experimental results show that BERT outperforms LDA in terms of Coherence.

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Section: Related Workmentioning

confidence: 99%