Text Summarization of News Events Using Semantic Triples

International Journal of Computer Trends and Technology (IJCTT)          
© 2021 by IJCTT Journal
Volume-69 Issue-5
Year of Publication : 2021
Authors : Shikha Singh, Garima Srivastava

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

APA Style:   
Shikha Singh & Garima Srivastava 
(2021) . Text Summarization of News Events Using Semantic Triples.  International Journal of Computer Trends and Technology , 69(5), 77-81. 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.


[1] Amato, F., d’Acierno, A., Colace, F., Moscato, V., Penta, A., Picariello, A.: Semantic summarization of news from heterogeneous sources. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. LNDECT, 1, 305–314.Springer, Cham (2017).
[2] Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings (2016)
[3] Horacio Saggion and Thierry Poibeau. 2013. Automatic text summarization: Past, present and future. In Multi-source, Multilingual Information Extraction and Summarization. Springer, 3–21
[4] Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text Summarization Branches Out: Proceedings of the ACL-04 Workshop. 74– 81.
[5] Genest, P.-E., Lapalme, G.: Framework for abstractive summarization using text-to-text generation. In: Proceedings of the Workshop on Monolingual Text-To-Text Generation, pp. 64– 73. Association for Computational Linguistics (2011).
[6] Khan, A., Salim, N., Kumar, Y.J.: A framework for multidocument abstractive summarization based on semantic role labelling. Appl. Soft Comput. 30, 737–747 (2015).
[7] Del Corro, L., Gemulla, R.: ClausIE: Clause-based open information extraction. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 355–366. ACM (2013).
[8] Kshirsagar, M., Thomson, S., Schneider, N., Carbonell, J., Smith, N.A., Dyer, C.: Frame-semantic role labeling withheterogeneous annotations. In: ACL, 2(2015) 218–224.
[9] Lin, C.-Y., Hovy, E.: Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 NAACL, vol. 1, pp. 71–78. Association for Computational Linguistics (2003).
[10] Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, 1–8. ACM (2011).
[11] Nenkova, A., Passonneau, R.J.: Evaluating content selection in summarization: The Pyramid method. In: HLT-NAACL, 4 (2004) 145–152.
[12] Oya, T., Mehdad, Y., Carenini, G., Ng, R.: A template-based abstractive meeting summarization: leveraging summary and source text relationships. In: Proceedings of the 8th International Natural Language Generation Conference (INLG), pp. 45–53. Association for Computational Linguistics, Philadelphia, June 2014.
[13] Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet::Similarity: Measuring the relatedness of concepts. In: Demonstration Papers at HLT-NAACL 2004, pp. 38–41. Association for Computational Linguistics (2004).
[14] Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.:. The Stanford coreNLP natural language processing toolkit. In: ACL (System Demonstrations), (2014) 55–60.
[15] Kshirsagar, M., Thomson, S., Schneider, N., Carbonell, J., Smith, N.A., Dyer, C.: Frame-semantic role labeling with heterogeneous annotations. In: ACL, 2 (2015) 218–224.