GraphSUM: Scalable Graph Summarization for Efficient Question Answering

Efficiently processing large-scale graphs for question-answering tasks presents a significant challenge, given the complexity and volume of data involved in such graphs. This paper presents a new framework that combines attention-based graph summarization with innovative graph sampling methods designed specifically for large-scale graph processing and question-answering applications. Our approach excels in its ability to process large-scale graphs efficiently, leveraging effective sampling and attention mechanisms to enhance feature extraction. A key aspect of our approach is graph summarization techniques, which concentrate on essential information, boosting the accuracy and computational efficiency of question answering. Our framework proves its efficacy in real-world scenarios through practical demonstrations, notably within academic databases. This showcases a substantial advancement in information retrieval and graph-based data navigation, marking a significant leap forward in the field.