Abstract
In this paper, we are proposing a hybrid model of latent semantic analysis with graph-based xtractive text summarization on Telugu text. Latent semantic analysis (LSA) is an unsupervised method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a corpus of text. Text rank algorithm is one of the graph-based ranking algorithm which is based on the similarity scores of the sentences. This hybrid method has been implemented on Eenadu Telugu e-news data. The ROUGE-1 measures are used to evaluate the summaries of proposed model and human-generated summaries in this extractive text summarization. The proposed LSA with Text rank method has a F1-score of 0.97 as against the F1-score of 0.50 for LSA and 0.49 of Text rank methods. The hybrid model yields better performance compared with the individual algorithms of latent semantic analysis and Text rank results.
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Lakshmi, A., Latha, D. (2023). A Hybrid Model of Latent Semantic Analysis with Graph-Based Text Summarization on Telugu Text. In: Bhateja, V., Sunitha, K.V.N., Chen, YW., Zhang, YD. (eds) Intelligent System Design. Lecture Notes in Networks and Systems, vol 494. Springer, Singapore. https://doi.org/10.1007/978-981-19-4863-3_17
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DOI: https://doi.org/10.1007/978-981-19-4863-3_17
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