Extractive Summarization using Deep Learning

TL;DR

This paper introduces a deep learning-based extractive summarization method for factual reports, utilizing feature extraction, enhancement with Restricted Boltzmann Machines, and sentence scoring to generate coherent summaries.

Contribution

It presents a novel three-phase approach combining feature enhancement with RBMs to improve extractive summarization accuracy.

Findings

Effective summary generation demonstrated on multiple articles

Enhanced feature set improves sentence selection accuracy

Source code available for reproducibility

Abstract

This paper proposes a text summarization approach for factual reports using a deep learning model. This approach consists of three phases: feature extraction, feature enhancement, and summary generation, which work together to assimilate core information and generate a coherent, understandable summary. We are exploring various features to improve the set of sentences selected for the summary, and are using a Restricted Boltzmann Machine to enhance and abstract those features to improve resultant accuracy without losing any important information. The sentences are scored based on those enhanced features and an extractive summary is constructed. Experimentation carried out on several articles demonstrates the effectiveness of the proposed approach. Source code available at: https://github.com/vagisha-nidhi/TextSummarizer