A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26087

Title:	A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling
Authors:	Onah, DFO Pang, ELL El-Haj, M
Keywords:	summarization;extractive;abstractive;latent Dirichlet;allocation;topic modelling;visualisation;ROUGE
Issue Date:	17-Dec-2022
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Citation:	Onah, D.F.O., Pang, E.L.L. and El-Haj, M. (2022) 'A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling', 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 17-20 December 2022, pp. 1 - 11. doi: 10.1109/BigData55660.2022.10020259.
URI:	https://bura.brunel.ac.uk/handle/2438/26087
DOI:	https://doi.org/10.1109/BigData55660.2022.10020259
ISBN:	978-1-6654-8045-1 (ebk)
ISSN:	978-1-6654-8046-8 (PoD)
Appears in Collections:	The Brunel Collection

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