Graph Neural Network and NER-Based Text Summarization

TL;DR

This paper presents a novel text summarization method that combines Graph Neural Networks and Named Entity Recognition to improve the relevance and efficiency of condensed textual representations.

Contribution

It introduces an innovative approach integrating GNNs and NER for enhanced text summarization, addressing the challenge of summarizing large documents effectively.

Findings

Improved relevance in summaries through entity emphasis

Enhanced summarization efficiency with GNNs

Potential for better handling of large textual datasets

Abstract

With the abundance of data and information in todays time, it is nearly impossible for man, or, even machine, to go through all of the data line by line. What one usually does is to try to skim through the lines and retain the absolutely important information, that in a more formal term is called summarization. Text summarization is an important task that aims to compress lengthy documents or articles into shorter, coherent representations while preserving the core information and meaning. This project introduces an innovative approach to text summarization, leveraging the capabilities of Graph Neural Networks (GNNs) and Named Entity Recognition (NER) systems. GNNs, with their exceptional ability to capture and process the relational data inherent in textual information, are adept at understanding the complex structures within large documents. Meanwhile, NER systems contribute by identifying and emphasizing key entities, ensuring that the summarization process maintains a focus on the most critical aspects of the text. By integrating these two technologies, our method aims to enhances the efficiency of summarization and also tries to ensures a high degree relevance in the condensed content. This project, therefore, offers a promising direction for handling the ever increasing volume of textual data in an information-saturated world.