A knowledge-graph based text summarization scheme for mobile edge computing

• Published in: Journal of cloud computing : advances, systems and applications Vol. 13; no. 1; pp. 9 - 15
• Main Authors: Yu, Zheng, Wu, Songyu, Jiang, Jielin, Liu, Dongqing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Artificial intelligence; Artificial neural networks; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Data integration; Data processing; Documents; Edge computing; Graph theory; Information Systems Applications (incl.Internet); Knowledge graph; Knowledge representation; Mobile computing; Mobile edge computing; Mobile Edge Computing Meets AI; Natural language processing; Semantics; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Natural language processing; Artificial intelligence; Mobile edge computing; Knowledge graph
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-023-00585-6

As the demand for edge services intensifies, text, being the most common type of data, has seen a significant expansion in data volume and an escalation in processing complexity. Furthermore, mobile edge computing (MEC) service systems often faces challenges such as limited computational capabilities and difficulties in data integration, requiring the development and implementation of more efficient and lightweight methodologies for text data processing. To swiftly extract and analysis vital information from MEC text data, an automatic generation scheme of multi-document text summarization based on knowledge graph is proposed in this paper, named KGCPN. For the text data from MEC devices and applications, the natural language processing technology is used to execute the data pre-processing steps, which transforms the MEC text data into a computationally tractable and semantically comprehensible format. Then, the knowledge graph of multi-document text is constructed by integrating the relationship paths and entity descriptions. The nodes and edges of the knowledge graph serve to symbolize the semantic relationships within the text, and the Graph Convolution Neural network (GCN) is used to understand the text and learn the semantic representation. Finally, a pointer-generator network model accepts the encoding information from GCN and automatically generate a general text summarization. The experimental results indicate that our scheme can effectively facilitate the smart pre-processing and integration of MEC data.