ConHGNN-SUM: A Contextualized Heterogeneous Graph Neural Network for Extractive Text Summarization

Text summarization is a valuable method for extracting important details from large volumes of text data, facilitating tasks like text data analysis. Various text summarization techniques have been developed over time, with some focusing on selecting and summarizing short sentences, while others overlook the semantic relationship between sentences. Extractive document summarization involves learning cross-sentence relations, a critical aspect that has been extensively explored using various approaches. One effective method is to employ neural networks based on graphs, which offer an intricate structure capable of obtaining relations among sentences. In this paper, we present a contextualized heterogeneous graph neural network for extractive text summarization (ConHGNN-SUM), incorporating semantic nodes that extend beyond individual sentences, and emphasizes the importance of capturing the relationship between selected sentences as a final step in the summarization process. These extra nodes function as intermediaries connecting sentences and enhancing the interrelationships between them. Our model enhances conventional graph-based extractive methods and delivers comparable performance to other advanced systems for extractive summarization.