Open Access Open Access  Restricted Access Subscription Access

Multimedia Summarization using Lex Rank Algorithm and Latent Semantic Analysis for e-Learning

Sharon . M, Madhura M Lambe, Muneeza Mushtaq, Lithima Murugan

Abstract


Chunks of Information are readily accessible online, it is very crucial to provide a solution to get information most efficiently and accurately. With more people using the internet and smartphones, there has been a consistent increase in online learning, entertainment, and various other things. But sometimes we do not have the time to go through all the material such as videos or podcasts in case it was too long. Therefore, something must be able to condense lengthy assessments into concise statements that convey the same idea. In this regard, automatic summarization can be helpful. The main objective of Automatic Summarization is extracting Summaries from Large chunks of digital data efficiently and accurately. We are achieving this, using Natural Language Processing techniques using LexRank Algorithm and Latent Semantic Analysis.

 


Full Text:

PDF

References


Madhuri, J. N., & Ganesh Kumar, R. (2019). Extractive Text Summarization Using Sentence Ranking. 2019 International Conference on Data Science and Communication (IconDSC).

Jadhav, A., Jain, R., Fernandes, S., & Shaikh, S. (2019). Text Summarization using Neural Networks. 2019 International Conference on Advances in Computing, Communication and Control (ICAC3).

S M, M., M P, R., R E, A., & G SR, E.S. (2020). Text Summarization Using Text Frequency Ranking Sentence Prediction. 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP).

N. Gonuguntla, B. Mandal, N. Puhan et al., “Enhanced Deep Video Summarization Network,” in 2019 British Machine Vision Conf. (BMVC), 2019.

Agyeman, R., Muhammad, R., & Choi, G. S. (2019, March). Soccer Video Summarization Using Deep Learning. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 270- 273). IEEE.

Emad, A., Bassel, F., Refaat, M., Abdelhamed, M., Shorim, N., & AbdelRaouf, A. (2021). Automatic Video summarization with Timestamps using natural language processing text fusion. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). doi:10.1109/ccwc51732.2021.9376115

Awasthi, I., Gupta, K., Bhogal, P. S., Anand, S. S., & Soni, P. K. (2021). Natural Language Processing (NLP) based Text Summarization - A Survey. 2021 6th International Conference on Inventive Computation Technologies (ICICT).

L’Huillier, G., Hevia, A., Weber, R., & Rios,S. (2010). Latent semantic analysis and keyword extraction for phishing classification. 2010 IEEE International Conference on Intelligence and Security Informatics.

Sah, S., Kulhare, S., Gray, A., Venugopalan, S., Prud’Hommeaux, E., & Ptucha, R. (2017). Semantic Text Summarization of Long Videos. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2017.115

Jiang, Y., Cui, K., Peng, B., & Xu, C. (2019). Comprehensive Video Understanding: Video Summarization with Content-Based Video Recommender Design. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).


Refbacks

  • There are currently no refbacks.