Extractive Summarization of Bible Data using Topic Modeling

Extractive Summarization of Bible Data using Topic Modeling

© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : Vasantha Kumari Garbhapu, Prajna Bodapati
DOI : 10.14445/22315381/IJETT-V70I6P210

How to Cite?

Vasantha Kumari Garbhapu, Prajna Bodapati, "Extractive Summarization of Bible Data using Topic Modeling," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 79-89, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P210

To attain a sense of balance among summary quality and machine readability to preserve the sentence structure and topic similarity, this work presents a statistical and topic modeling-based strategy to extract automatic summarization using the English Bible data set. First, it proposes an algorithm to generate an automatic summary. The measure's core is covered by the Latent Dirichlet Allocation (LDA) method that can capture the most important topics. After that, the summary methods are ranked by the quantity to which the most important topics of their summaries are similar to the most important topics of the reference document. Then, the work focuses primarily on evaluating the summary quality by the ROUGE metric and co-selection measures like Precision, F1 score, and Recall. The evaluation results show that the proposing algorithm has better results with ROUGE score, topic similarity, and manual summary than LSA and TextRank algorithms. Furthermore, this algorithm is competent in computational processing and an understandable method for implementing the English Bible dataset that has not been studied previously.

Automatic Extract, Latent Dirichlet Allocation (LDA), ROUGE, Summary Evaluation, Text Summarization.

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