A knowledge-graph based text summarization scheme for mobile edge computing
Abstract 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 ca...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-01-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-023-00585-6 |
| _version_ | 1827388169088139264 |
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| author | Zheng Yu Songyu Wu Jielin Jiang Dongqing Liu |
| author_facet | Zheng Yu Songyu Wu Jielin Jiang Dongqing Liu |
| author_sort | Zheng Yu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T16:13:48Z |
| format | Article |
| id | doaj.art-e14373b4a36b4d8ca7428793f96e87af |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-03-08T16:13:48Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-e14373b4a36b4d8ca7428793f96e87af2024-01-07T12:47:11ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2024-01-0113111510.1186/s13677-023-00585-6A knowledge-graph based text summarization scheme for mobile edge computingZheng Yu0Songyu Wu1Jielin Jiang2Dongqing Liu3School of Software, Nanjing University of Information Science and TechnologySchool of Software, Nanjing University of Information Science and TechnologySchool of Software, Nanjing University of Information Science and TechnologyInstitute of Special Weather Climate (ISWC), Nanjing UniversityAbstract 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.https://doi.org/10.1186/s13677-023-00585-6Mobile edge computingArtificial intelligenceKnowledge graphNatural language processing |
| spellingShingle | Zheng Yu Songyu Wu Jielin Jiang Dongqing Liu A knowledge-graph based text summarization scheme for mobile edge computing Journal of Cloud Computing: Advances, Systems and Applications Mobile edge computing Artificial intelligence Knowledge graph Natural language processing |
| title | A knowledge-graph based text summarization scheme for mobile edge computing |
| title_full | A knowledge-graph based text summarization scheme for mobile edge computing |
| title_fullStr | A knowledge-graph based text summarization scheme for mobile edge computing |
| title_full_unstemmed | A knowledge-graph based text summarization scheme for mobile edge computing |
| title_short | A knowledge-graph based text summarization scheme for mobile edge computing |
| title_sort | knowledge graph based text summarization scheme for mobile edge computing |
| topic | Mobile edge computing Artificial intelligence Knowledge graph Natural language processing |
| url | https://doi.org/10.1186/s13677-023-00585-6 |
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