/TEXT-SUMMARIZATION-USING-RECURRENT-NEURAL-NETWORK

TEXT SUMMARIZATION USING RECURRENT NEURAL NETWORK

Primary LanguageJupyter Notebook

Text Summerization Using Recurrent Neural Networks

Text summarization is a ubiquitous application. It can be used for news summarization, product review, youtube video title generation and more. In this project, we built a model that summarizes a given document or a given text paragraph. There are various ways to do this; we used a neural network approach. We uses a Recurrent Neural Network with gated recurrent unit (GRU) and compared the results with the Long Short Term Memory(LSTM) model for the same datasets. We applied text summarization on CNN/Daily News dataset and extended it on Amazon Fine Food Reviews.

References for models:

  1. https://github.com/himanshurawat443/Abstractive-Text-Summarization-using-seq-2-seq-RNN-s/issues
  2. https://www.kaggle.com/code/singhabhiiitkgp/text-summarization-using-lstm/notebook