Abstract
The Internet has become one of the important channels for users to access to information and knowledge. It is crucial that how to acquire key content accurately and effectively in the events from huge amount of network information. This paper proposes an algorithm for automatic generation of network document summaries based on knowledge graph and TextRank algorithm which can solve the problem of information overload and resource trek effectively. We run the system in the field of big data application in packaging engineering. The experimental results show that the proposed method KG-TextRank extracts network document summaries more accurately, and automatically generates more readable and coherent natural language text. Therefore, it can help people access information and knowledge more effectively.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China under grant number 61502163, in part by the Hunan Provincial Natural Science Foundation of China under grant numbers 2016JJ5035, 2016JJ3051 and 2015JJ3046, in part by the Hunan Provincial Science and Technology Project under grant number 2015TP1003, in part by the Project of China Packaging Federation under Funding Support Numbers 2014GSZJWT001KT010, 17ZBLWT001KT010, in part by the National Packaging Advertising Research Base and Hunan Packaging Advertising Creative Base under grant number 17JDXMA03, in part by the Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory under grant number 2017KF07.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wu, Y., Chen, R., Li, C., Chen, S., Zou, W. (2019). Automatic Summarization Generation Technology of Network Document Based on Knowledge Graph. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_3
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DOI: https://doi.org/10.1007/978-3-030-19086-6_3
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