# Abstractive Text Summarization using Attentive GRU based Encoder-Decoder

## TL;DR

This paper presents an abstractive text summarization model using an attentive GRU encoder-decoder architecture, which effectively handles long sequences and outperforms existing models on news datasets.

## Contribution

Introduces a novel attentive GRU-based encoder-decoder model for abstractive summarization with improved handling of long input sequences.

## Key findings

- Outperforms existing models on news summarization datasets
- Handles long sequences effectively with attention mechanism
- Generates summaries comparable to newspaper headlines

## Abstract

In todays era huge volume of information exists everywhere. Therefore, it is very crucial to evaluate that information and extract useful, and often summarized, information out of it so that it may be used for relevant purposes. This extraction can be achieved through a crucial technique of artificial intelligence, namely, machine learning. Indeed automatic text summarization has emerged as an important application of machine learning in text processing. In this paper, an english text summarizer has been built with GRU-based encoder and decoder. Bahdanau attention mechanism has been added to overcome the problem of handling long sequences in the input text. A news-summary dataset has been used to train the model. The output is observed to outperform competitive models in the literature. The generated summary can be used as a newspaper headline.