Hybrid Optimization Based Hindi Document Summarization Using Deep Learning Technique
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Abstract
The proliferation of textual information today is a result of the internet's recent development, which is widely accessible to anybody, at any time. Generally speaking, several Natural Language Processing (NLP) techniques can be used to analyze the textual information that is offered on the basis of text documents. In recent years, various text summarization techniques have been implemented in English text documents but a little amount of work is carried out in Hindi text documents summarization. In this research investigation, the Coot Remora Optimization (CRO) technique based on Deep Recurrent Neural Network (DRNN) is used to summarize Hindi documents. Here, the CRO algorithm is used to train the DRNN, which is used to compute the sentence scores.The highest scored sentences are going to included in the summary. When compared to recent optimization algorithmic techniques, such as MCRMR-SSO, Graph-based_PSO, Genetic Algorithms (GA), and Political Elephant Herding Optimization (PEHO) based Deep Long Short Term Memory (DLSTM) algorithm, the developed method is shown to be superior. Additionally, three evaluation metrics such as precision, recall, f-measure are used to analyze the performance of the CRO based DRNN technique and obtained high performance.
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References
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