Text Summarization of News Events Using Semantic Triples

© 2021 by IJCTT Journal
Volume-69 Issue-5
Year of Publication : 2021
Authors : Shikha Singh, Garima Srivastava
DOI :  10.14445/22312803/IJCTT-V69I5P111

How to Cite?

Shikha Singh, Garima Srivastava, "Text Summarization of News Events Using Semantic Triples," International Journal of Computer Trends and Technology, vol. 69, no. 5, pp. 77-81, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I5P111

Text summarization is basically used to generate a compact version of the original document for the article. The summarization task can be challenging due to the same report is generated by different people of diverse opinions. But here, major issues are to rectify redundant information or relevant information for text summarization. Currently, many techniques are available in the market, and in which modeling events as semantic triples is one of them. In semantic triple, triples are weighted Based on frequencies and then to form a summary. Generally, triples are extracted from the statement of the report which may sometime lose important information. This paper focuses on lossless summarisation with the help of graph structure. Summary sentences are generated by picking the top-rated path from a complete structured graph with the maximum number of triples and grammatical correctness. Here we have also done several improvements to rectify the limitations of the model. We have included entity linking and verb linking to eliminate the limitation of coverage, correctness, and grammaticality.

Extractive summary, Abstractive summary, PSB, PGF.


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