Abstract

Summarizing involves condensing a text while retaining its key points. Extractive summarizers focus on identifying important sentences from the text to convey its message effectively. They typically operate by identifying keywords and selecting sentences containing those keywords prominently. Keyword extraction entails identifying significant words with higher frequencies, particularly emphasizing important ones. In this system, a TF (Term Frequency) model and GSS coefficients were employed to extract keywords and rank text. The algorithm automatically extracts keywords for summarizing texts in Kannada datasets.

References

• J.N.Madhuri, Ganesh Kumar.R, “Extractive Text Summarization Using Sentence Ranking,” International Conference on Data Science and Communication (IconDSC), 2019.
• Mahsa Afsharizadeh, Hossein Ebrahimpour-Komleh, Ayoub Bagheri, “Query-oriented Text Summarization using Sentence,” 4th International Conference on Web Research (ICWR), 2018.
• Ping Chen, Fei Wu, Tong Wang, Wei Ding “A Semantic QA-Based Approach for Text Summarization Evaluation,” The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2019.
• Md Ashraful Islam Talukder, Sheikh Abujar, Abu Kaisar Mohammad Masum, Fahad Faisal, Syed Akhter Hossain, “Bengali abstractive text summarization using sequence to sequence RNNs,” 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019.
• Scott A. Crossley, “Automated Summarization Evaluation (ASE) Using Natural Language Processing Tools,” International Conference on Ar‹t#if›icial Intelligence in Education, 2019.
• Rupali Hande, “Two-Level Text Summarization with Natural Language Processing,” International Conference on Computer Networks and Inventive Communication Technologies, 2019.
• Avinash Payak, “Automatic Text Summarization and Keyword Extraction using Natural Language Processing,” International Conference on Electronics and Sustainable Communication Systems, 2020.
• Vishal Soni, “Text Summarization: An Extractive Approach,” Advances in Intelligent Systems and Computing, 2020.
• K. Janaki Raman, “Automatic Text Summarization of Article (NEWS) Using Lexical Chains and WordNet,” Artificial Intelligence Techniques for Advanced Computing Applications, 2020.
• Sagarika Pattnaik, “Automatic Text Summarization for Odia Language: A Novel Approach,” Intelligent and Cloud Computing, Smart Innovation, Systems and Technologies, 2021.
• M. Lu and F. Li, "Survey on lie group machine learning," in Big Data Mining and Analytics, vol. 3, no. 4, pp. 235-258, Dec. 2020, doi: 10.26599/BDMA.2020.9020011.
• Jayashree. R, Srikanta Murthy.K and Sunny.K, “Keyword Extraction based Summarization of Categorized Kannada Text Documents”, International journal on soft computing (IJSC), Vol.2, N0.4, November 2011.
• Letian Wang, Fang Li, SJTULT LAB: “ Chunk Based Method for Keyphrase Extraction”, Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010,pp 158– 161,Uppsala, Sweden,15-16 July 2010.
• You Ouyang Wenjie Li Renxian Zhang,'273. Task 5. “Key phrase Extraction Based on Core Word Identification and Word Expansion”, Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pp 142–145, Uppsala, Sweden, 15-16 July 2010.
• Rupali Hande, “Two-Level Text Summarization with Natural Language Processing,” International Conference on Computer Networks and Inventive Communication Technologies, 2019.
• Y. Ko, et al., “Automatic text categorization using the importance of sentences,” in Proceedings of the 19th International Conference on Computational Linguistics, Vol. 1, 2022, pp. 1-7.
• A. Kolcz, et al., “Summarization as feature selection for text categorization,” in Proceedings of the 10th International Conference on Information and Knowledge Management, 2021, pp. 365-370.
• D. Shen, et al., “Web-page classification through summarization,” in Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval, 2020, pp. 242-249.
• A. McCallum and K. Nigam, “A comparison of event models for Naïve Bayes text classification,” in Proceedings of AAAI Workshop on Learning for Text Categorization, 2020, pp. 41-48.
• R. R. Yager, “An extension of the naïve Bayesian classifier,” Information Sciences, Vol. 176, 2006, pp. 577-588.
• T. Joachims, “A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization,” in Proceedings of the 14th International Conference on Machine Learning, 1997, pp. 143-151.
• I. Rahal and W. Perrizo, “An optimized approach for KNN text categorization using P-trees,” in Proceedings of ACM Symposium on Applied Computing, 2004, pp. 613-617.
• E. Gabrilovich and S. Markovitch “Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5,” in Proceedings of the 21st International Conference on Machine Learning, 2004, pp. 321-328.