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Text Summarization of Articles Using LSTM and Attention-Based LSTM by Harsh Kumar; Gaurav Kumar; Shaivye Singh; Sourav Paul is a book available to read on EtoBox.

What is Text Summarization of Articles Using LSTM and Attention-Based LSTM about?

This research work focuses on developing a model for performing text summarization of articles, depending on the novelty of the information that is arranged in the final summary, where summarization is roughly categorized into two types: extractive and abstractive summarization. In today's fast-paced society, most people disregard if they are deemed to be rather extensive due to a lack of time. Therefore, having a brief overview of this article would not only encourage readers to read it, but if the topic is of interest, it would but also allow them to go deeper into it. As a result, this research work intends to bridge this research gap with the proposed model. Initially, this research project developed a reference RNN model and tested its accuracy. It was discovered that the summary loses a lot of real terms and reduces and confuses the overall meaning, therefore it was decided to build an LSTM model for the same and test it with three distinct datasets. The first experiment is carried out with a small dataset, whereas the second experiment is carried out with a comparatively bigger dataset by applying LSTM to it. We decided to carry it forward and improve the model by increasing

Author

Harsh Kumar; Gaurav Kumar; Shaivye Singh; Sourav Paul

Publisher

Springer Singapore : Imprint: Springer

Published

2022

Language

EN

ISBN

9789811679957

Subjects

Mathematics, Engineering, Computer Science