International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 16, No. 4, 8 Aug. 2024
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Document Summarization Based on Information Retrieval Using Query Search Ranking Method
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Author(s)
Surya S.
1,*
Sumitra P.
1
Index Terms
Information Retrieval (IR), Query, Relating Keyword Query Search Summarization (RKQSS), Word Frequency (WF), Query Searching Based Ranking Summarization Data Retrieval (QS-RSDR)
Abstract
This work proposes a Query Searching Based Ranking Summarization Data Retrieval (QS-RSDR) method for document summarization based on information retrieval. QS-RSDR ranks query-based retrieval of important information, enabling the creation of a detailed report of information requirements using generated sections of sample documents. Relating Keyword Query Search Summarization (RKQSS) generates the main summary from the most relevant document in the query and then the summary from the other documents. The method resolves similarity terms related to the query using the Word Frequency (WF) method. Sentence ranking weights and sentence frequency improve the accuracy of the retrieved documents. Simulation results show improved accuracy in information retrieval. The proposed method can help address unclear and short queries and understand the nature of the required information behind the query. The paper concludes that QS-RSDR is an effective solution for document summarization based on information retrieval using the query search ranking method.
Cite This Paper
Surya S., Sumitra P., "Document Summarization Based on Information Retrieval Using Query Search Ranking Method", International Journal of Modern Education and Computer Science(IJMECS), Vol.16, No.4, pp. 58-70, 2024. DOI:10.5815/ijmecs.2024.04.05
Reference
[1]R. Das, A. Godbole, D. Kavarthapu, Z. Gong, A. Singhal, M. Yu, and A. McCallum, “Multi-step entity-centric information retrieval for multi-hop question answering,” In Proceedings of the 2nd Workshop on Machine Reading for Question Answering pp. 113-118, 2019. DOI:10.18653/v1/D19-5816
[2]K. Roitero, E. Maddalena, S. Mizzaro, and F. Scholer, “On the effect of relevance scales in crowdsourcing relevance assessments for Information Retrieval evaluation,” Inf Process Manag, vol. 58(6), pp. 102688, 2021. https://doi.org/10.1016/j.ipm.2021.102688
[3]M. Zhao, S. Yan, B. Liu, X. Zhong, Q. Hao, H. Chen, and W. Guo, “QBSUM: A large-scale query-based document summarization dataset from real-world applications,” Comput Speech Lang, vol. 66, pp. 101166, 2021. https://doi.org/10.1016/j.csl.2020.101166
[4]M. Mohd, R. Jan, and M. Shah, “Text document summarization using word embedding,” Expert Syst. Appl, vol. 143, pp. 112958, 2020. https://doi.org/10.1016/j.eswa.2019.112958
[5]R. C. Belwal, S. Rai, and A. Gupta, “A new graph-based extractive text summarization using keywords or topic modeling,” Ambient Intell Humaniz Comput, vol. 12(10), pp. 8975-8990, 2021. https://doi.org/10.1007/s12652-020-02591-x
[6]A.Al-Saleh, and M. E. B. Menai, “Solving multi-document summarization as an orienteering problem,” Algorithms, vol. 11(7), pp. 96, 2018. https://doi.org/10.3390/a11070096
[7]K. Manju, S. David Peter, and S. M. Idicula, “A framework for generating extractive summary from multiple Malayalam documents.” Information, vol. 12(1), pp. 41, 2021. https://dx.doi.org/10.3390/info12010041
[8]H. Van Lierde, and T. W. Chow, “Query-oriented text summarization based on hypergraph transversals,” Inf Process Manag, vol. 56(4), pp. 1317-1338, 2019. https://doi.org/10.1016/j.ipm.2019.03.003
[9]K. Kumar, “Text query based summarized event searching interface system using deep learning over cloud,” Multimed. Tools Appl, vol. 80(7), pp. 11079-11094. 2021. https://doi.org/10.1007/s11042-020-10157-4
[10]D. Patel, S. Shah, and H. Chhinkaniwala, “Fuzzy logic based multi document summarization with improved sentence scoring and redundancy removal technique,” Expert Syst. Appl, vol. 134, pp. 167-177, 2019. https://doi.org/10.1016/j.eswa.2019.05.045
[11]A.P.Widyassari, S. Rustad, G. F. Shidik, E. Noersasongko, A. Syukur, and A. Affandy, “Review of automatic text summarization techniques & methods.” J. King Saud Univ., Comp,inf. Sci, vol. 34(4), pp.1029-1046, 2022. https://doi.org/10.1016/j.jksuci.2020.05.006
[12]F. Bayatmakou, A. Mohebi, and A. Ahmadi, “An interactive query-based approach for summarizing scientific documents,” Inf. Discov, vol. 50(2), pp. 176-191, 2022. https://doi.org/10.1108/IDD-10-2020-0124
[13]J. A. Jilo, A. T. Alemu, F. Z. Abera, and F. Rashid, “An integrated development of a query-based document summarization for Afaan Oromo using morphological analysis,” Indian J Sci Technol, vol. 14(38), pp. 2946-2952, 2021. https://doi.org/10.17485/IJST/v14i38.1182
[14]Y. Li, J. Ma, and Y. Zhang, “Image retrieval from remote sensing big data: A survey.” Inf Fusion, vol. 67, pp.94-115, 2021.https://doi.org/10.1016/j.inffus.2020.10.008
[15]A.Qaroush, I. A. Farha, W. Ghanem, M. Washaha, and E. Maali, “An efficient single document Arabic text summarization using a combination of statistical and semantic features,” Journal of King Saud University-Computer and Information Sciences, vol. 33(6), pp. 677-692, 2021. https://doi.org/10.1016/j.jksuci.2019.03.010
[16]W. S. El-Kassas, C. R. Salama, A. A. Rafea, and H. K. Mohamed, “Automatic text summarization: A comprehensive survey,” Expert Syst. Appl, vol. 165, pp. 113679, 2021. https://doi.org/10.1016/j.eswa.2020.113679
[17]N. Rahman, and B. Borah, “Improvement of query-based text summarization using word sense disambiguation,” COMPLEX INTELL SYST, vol. 6, pp. 75-85, 2020. https://doi.org/10.1007/s40747-019-0115-2
[18]A.Sutton, M. Clowes, L. Preston, and A. Booth, “Meeting the review family: exploring review types and associated information retrieval requirements,” COMPLEX INTELL SYST, vol. 36(3), pp. 202-222, 2019. https://doi.org/10.1111/hir.12276
[19]S. Murarka, and A. Singhal, “Query-based single document summarization using hybrid semantic and graph-based approach,” In 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM), pp. 330-335, 2020. IEEE. DOI: 10.1109/ICACCM50413.2020.9212923
[20]P. Tampakis, D. Spyrellis, C. Doulkeridis, N. Pelekis, C. Kalyvas, and A. Vlachou, “A novel indexing method for spatial-keyword range queries,” In 17th International Symposium on Spatial and Temporal Databases, pp. 54-63, 2021. https://doi.org/10.1145/3469830.3470897
[21]J. A. Nasir, I. Varlamis, and S. Ishfaq, “A knowledge-based semantic framework for query expansion,” Inf Process Manag, vol. 56(5), pp. 1605-1617, 2019. https://doi.org/10.1016/j.ipm.2019.04.007
[22]J. Guo, Y. Fan, L. Pang, L. Yang, Q. Ai, H. Zamani, and X. Cheng, “A deep look into neural ranking models for information retrieval,” Inf Process Manag, vol. 57(6), pp. 102067, 2020. https://doi.org/10.1016/j.ipm.2019.102067
[23]Z. Wu, S. Shen, X. Lian, X. Su, and E. Chen, “A dummy-based user privacy protection approach for text information retrieval,” Knowledge-Based Systems, vol. 195, pp. 105679, 2020. https://doi.org/10.1016/j.knosys.2020.105679
[24]R. Handa, C. R. Krishna, and N. Aggarwal, “Document clustering for efficient and secure information retrieval from cloud,” Concurr. Comput. Pract. Exp, vol. 31(15), pp. e5127, 2019. https://doi.org/10.1002/cpe.5127
[25]D. R. Ganesh, and M. Chithambarathanu, “A Survey on Hybrid PSO and SVM Algorithm for Information Retrieval.” In Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2022 pp. 121-130, 2022. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6004-8_11