Recurrent neural network for abstractive summarization of documents

Abstract

Automatic Text Summarization is the process which utilizes machine power to process large paragraphs in order to create brief and refined summary. Text summarization can be classified into two classes viz. extractive and abstractive. The pith of the extractive is a choice issue, which consequently picks significant sentences from the input content as indicated by different assessment measures; while abstractive requires a profound semantic and talk comprehension of the content to create a base rephrased summary. Deep learning gives an achievable system to create an abstractive summarizer. Intermittent neural system Recurrent Neural Network (RNN) based arrangement to-grouping number of layers for learning has made as rounding progress in different natural language processing errands. The proposed model principally comprises of two sections - an encoder and a decoder - each one of which is a Recurrent Neural Network (RNN). ROUGE assessment metric is utilized to assess the likeness between real feature and anticipated feature.

Subject Classification:

• 68T99

Keywords:

• Abstractive Summarization
• RNN
• encoder-decoder
• Sequential Model