Multimedia Abstraction in E-learning via LexRank and Latent Semantic Analysis

Authors

  • Madhura M. Lambe Student, Department of Computer Science & Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India
  • Muneeza Mushtaq Student, Department of Computer Science & Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India
  • Deepak N. R. Professor, Department of Computer Science & Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India
  • Simran Pal R. Professor, Department of Computer Science & Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India

Keywords:

NLP, LexRank, Latent Semantic Analysis, Automatic summarization, deep learning

Abstract

In today’s era of abundant online information, it is essential to offer an efficient and precise solution for accessing information. With the growing usage of the Internet and smartphones, there has been a consistent rise in online education, entertainment, and various other activities. However, there are instances when we lack the time to delve into lengthy content like videos or podcasts. Hence, there is a need for a method to condense lengthy materials into brief statements while retaining the original meaning. Automatic summarization comes into play here. Its primary goal is to extract concise summaries from vast amounts of digital data accurately and efficiently. To achieve this, we employ natural language processing techniques, utilizing algorithms like LexRank and latent semantic analysis.

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Published

2023-10-09