Optimized Text Summarization using Customized Recurrent Neural Network Model
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Abstract
In current era, the growth of big data is raising in large scale and it becomes tough to the users to load and draw conclusions from it. An efficient approach is required to generate short and precise summaries in user required form. In recent years, Sequence to Sequence models gained lot of focus
on text summarization to handle various challenges like fluency, human readability and also generation of optimistic summaries. The proposed approach effectively handled these issues using Customized Recurrent Neural Networks (C-RNNs) model to generate optimistic text summary. The
proposed model generated optimized text summaries where the data collected from social media/E-Commerce sites. C-RNN model are highly demanded for entrepreneur’s and consumers when they need precise text summaries. Experimental results show that proposed model is outperformed
the state of the art models in terms of syntactic and semantic structure and achieves qualitative results.
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