  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.
