Abstract
Latent semantic analysis has been used successfully for extractive text summarization for years, while random indexing-based summarization has been recently proposed in the literature for text summarization. The random indexing-based summarization inherently uses graph-based ranking techniques. In this paper, we propose a hybrid technique of latent semantic analysis and random indexing for text summarization. Further, we have performed experiments to compare the results with several related baseline methods. The effectiveness of the hybrid method so developed is evident from the relative increase in the results over the baseline LSA-based technique.
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Chatterjee, N., Yadav, N. (2019). Hybrid Latent Semantic Analysis and Random Indexing Model for Text Summarization. In: Fong, S., Akashe, S., Mahalle, P. (eds) Information and Communication Technology for Competitive Strategies. Lecture Notes in Networks and Systems, vol 40. Springer, Singapore. https://doi.org/10.1007/978-981-13-0586-3_15
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DOI: https://doi.org/10.1007/978-981-13-0586-3_15
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