Extractive Text Summarization using Neural Networks

TL;DR

This paper introduces a neural network-based extractive summarization method that is scalable and performs comparably to state-of-the-art models on standard datasets.

Contribution

It presents a fully data-driven neural network approach for single document summarization that can handle arbitrarily sized documents.

Findings

Achieved results comparable to state-of-the-art models on DUC 2002 dataset.

The model is scalable and can process arbitrarily large documents.

Uses a recursive approach by breaking documents into fixed-sized parts.

Abstract

Text Summarization has been an extensively studied problem. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feedforward neural networks for single document summarization. We train and evaluate the model on standard DUC 2002 dataset which shows results comparable to the state of the art models. The proposed model is scalable and is able to produce the summary of arbitrarily sized documents by breaking the original document into fixed sized parts and then feeding it recursively to the network.