Author(s)

Junhong Chen ^1,2, Kaihui Peng ³

Affiliation(s)

¹ School of Software Engineering, South China University of Technology, Guangzhou, China
² LeiHuo Studio, NetEase, Hangzhou, China
³ Faculty of Business and Economics, University of Malaya, Kuala Lumpur, Malaysia

Corresponding Author

Junhong Chen

ABSTRACT

This paper proposes a graph-based text summarization extraction algorithm. The algorithm is based on directed graphs and can incorporate the position information of sentences into the computational scope. When calculating the edge weights of nodes in the directed graph, a pre-trained model after negative sampling is used, which not only can extract deeper semantic features but also enable higher relevance between the contextual sentences in the article. The algorithm also introduces a weighting mechanism to adjust the extraction priority of the sentences according to the article's theme, resulting in a higher quality of extracted summary sentences that can represent the key information of the text as much as possible. The algorithm can capture the key information in the text, reduce the impact of irrelevant information on semantics, and play a role in text compression.

KEYWORDS

Text Summarization; Keyword Extraction; Pre-trained Model

CITE THIS PAPER

Junhong Chen, Kaihui Peng, Research on Graph-based Text Summarization Extraction Algorithm. Advances in Computer, Signals and Systems (2024) Vol. 8: 13-22. DOI: http://dx.doi.org/10.23977/acss.2024.080603.

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