Cited By
View all- Wu H(2024)Dilated convolution for enhanced extractive summarizationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23470946:2(4777-4790)Online publication date: 14-Feb-2024
Ensemble of Support Vector Machine and Ontological Structures to Generate Abstractive Text Summarization
Author: 
Amita AroraAuthors Info & Claims
Amita AroraAuthors Info & ClaimsPages 1 - 24
Abstract
Automatic summarization systems are much needed to lessen the information overload which is being faced by people due to exponential growth of data on World Wide Web. These systems choose the most significant part of the text from a single document or multiple documents and present the compressed surrogate form of the complete information which was intended to be conveyed. In this research paper, we propose an approach to generate summary from a given text first by extracting the most relevant sentences and then making further concise by creating ontological structures of these sentences and then generating the abstractive summary from these structures. Our proposed system is evaluated with DUC 2002 data set and it is found that the performance of this system as evaluated using ROUGE-1 is 58.175 which is better than other state of the art systems. The values reported in the experimental process of the research report the significant contribution of this innovative method.
References
[1]
A, A. (2020). Automatic Ontology Construction: Ontology From Plain Text Using Conceptualization and Semantic Roles. In Critical Approaches to Information Retrieval Research.
[2]
Alcon, O. L. E., & Lloret, E. (2018). Sempca-summarizer: Exploiting semantic Principal component analysis for Automatic summary generation. Computer Information, 37(5), 1126–1148.
[3]
Amit VhatkarP. B. (2020). Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples. Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, 130-136.
[4]
Amita Arora, M. S. (2017). Machine Learning Approach for Text Summarization. International Journal of Database Theory and Application, 10(8), 83–90.
[5]
A.P.S., R. Y. (2012). An Efficient Approach for Web document summarization by Sentence Ranking. International Journal of Advanced Research in Computer Science and Software Engineering, 2(7).
[6]
Arora, A. S., Singh, M., & Chauhan, N. (2017). Automatic Ontology Construction using Conceptualization and Semantic Roles. International Journal of Information Retrieval Research, 7(3), 62–80.
[7]
Babara, S. P. (2015). Improving Performance of Text Summarization. Procedia Computer Science, 354-363.
[8]
Balaji, J. T. G., Geetha, T. V., & Ranjani, P. (2014). Graph-Based Bootstrapping for Coreference Resolution. Journal of Intelligent Systems, 23(3), 293–310.
[9]
BanerjeeS. M. (2015). Multi-Document Abstractive Summarization Using ILP Based Multi-Sentence Compression. Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015).
[10]
Baralis, E. C., Cagliero, L., Jabeen, S., Fiori, A., & Shah, S. (2013). Multi-document summarization based on the Yago ontology. Expert Systems with Applications, 40(17), 6976–6984.
[11]
Canhasi, E. (2014). Graph-based models for multi-document summarization. PhD thesis.
[12]
Carlos-Francisco Méndez-Cruz, S. G.-C.-A.-P.-V.-J.-R.-V. (2017). First steps in automatic summarization of transcription factor properties for RegulonDB: Classification of sentences about structural domains and regulated processes. Database (Oxford), 2017, bax070. Advance online publication. 29220462.
[13]
Christian Smith, H. D. (2012). A More Cohesive Summarizer. COLING 2012.
[14]
ConroyJ. M. (2001). Using HMM and Logistic Regression to Generate Extract Summaries for DUC. Proceedings of the DUC 01.
[15]
Gambhir, M. G., & Gupta, V. (2017). Recent Automatic Text Summarization Techniques: A Survey. Artificial Intelligence Review, 47(1), 1–66.
[16]
GuptaA. K. (2014). Text Summarization Through Entailment-Based Minimum Vertex Cover. Proceedings of the Third Joint Conference on Lexical and Computational Semantics (SEM 2014). 10.3115/v1/S14-1010
[17]
HennigL. W. (2008). An Ontology-based Approach to Text Summarization. Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology - Workshops.
[18]
Ibrahim Altmami, N., & El Bachir Menai, M. J. (2020). Automatic summarization of scientific articles: A survey. Journal of King Saud University - Computer and Information Sciences.
[19]
Leskovec, J. M.-F. (2005). Extracting Summary Sentences Based on the Document Semantic Graph. Microsoft Research MSR-TR-2005-07.
[20]
Li, S. Y. O. (2007). Multi-document summarization using support vector regression. Proceedings of DUC.
[21]
Lin, C.-Y. (2003). Automatic evaluation of summaries using n-gram co-occurrence statistics. HLT-NAACL-PARALLEL '03: Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond, 3.
[22]
Lin, C.-Y. (2004). ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out. Association for Computational Linguistics.
[23]
Lloret, E. P., & Palomar, M. (2013). COMPENDIUM: A Text Summarisation Tool for Generating Summaries of Multiple Purposes, Domains, and Genres. Natural Language Engineering, 19(2), 147–186.
[24]
Manju, K., David Peter, S., & Mary Idicula, S. (2021). A Framework for Generating Extractive Summary from Multiple Malayalam Documents. Information (Basel), 12(1), 41.
[25]
Mihalcea, R. &. (2004). Textrank: Bringing order into texts. In Proceedings of EMNLP (pp. 404-411). Academic Press.
[26]
Over, P. D. H., Dang, H., & Harman, D. (2007). DUC in Context, Information Processing and Enhanced Graph Based Approach for Multi Document Summarization. Information Processing & Management, 43(6), 1506–1520.
[27]
ParveenD. &. (2015). Integrating Importance, Non-Redundancy and Coherence in Graph-Based Extractive Summarization. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015).
[28]
ParveenD. H.-M. (2015). Topical coherence for graph-based extractive summarization. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 10.18653/v1/D15-1226
[29]
Patil, M. S. (2014). A Hybrid Approach for Extractive Document Summarization Using Machine Learning and Clustering Technique. International Journal of Computer Science and Information Technologies, 5(2).
[30]
Ragunath, R. (2006). Ontology Based Text Document Summarization System,Using Concept Terms. Journal of Engineering and Applied Sciences (Asian Research Publishing Network).
[31]
Raj, M., Haroon, R., & Sobhana, N. (2020). A novel extractive text summarization system with self-organizing map clustering and entity recognition. Indian Academy of Sciences, 45(32).
[32]
Ramanujam, N. K. M., & Kaliappan, M. (2016). An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy. TheScientificWorldJournal, 2016, 1–10. 27034971.
[33]
Rodríguez-Vidal, J., Carrillo-de-Albornoz, J., Amigó, E., Plaza, L., Gonzalo, J., & Verdejo, F. (2020). Automatic generation of entity-oriented summaries for reputation management. Journal of Ambient Intelligence and Humanized Computing, 11(4), 1577–1591.
[34]
Sahoo, D., Bhoi, A., & Balabantaray, R. C. (2018). Hybrid approach to abstractive summarization. Procedia Computer Science, 132, 1228–1237.
[35]
SinghS. K. (2016). Bilingual Automatic Text Summarization Using Unsupervised Deep Learning. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – 2016. 10.1109/ICEEOT.2016.7754874
[36]
T., J. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. In Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence) (pp. 137-142). European Conference on Machine Learning.
[37]
Uçkana, K. A. (2020). Extractive multi-document text summarization based on graph independent sets. Egyptian Informatics Journal, 21(3), 145–157.
[38]
Van Lierde, T. C. (2019). Learning with fuzzy hypergraphs: A topical approach to query-oriented text summarization. Information Sciences, 496. 10.1016/j.ins.2019.05.020
[39]
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer.
[40]
Vázquez, E., Arnulfo García-Hernández, R., & Ledeneva, Y. (2018). Sentence features relevance for extractive text summarization using genetic algorithms. Journal of Intelligent & Fuzzy Systems, 35(1), 353–365.
[41]
Verma, R. C. (2009). A Semantic Free-text Summarization System Using Ontology Knowledge. IEEE Transactions on Information Technology in Biomedicine, 5(4), 261–270.
[42]
Wang, H. X. W. (2019). Self-supervised learning for contextualized extractive summarization. Association for Computational Linguistics, 2221–2227.
[43]
Furu Wei, M. Z. (2020, October). At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization Qingyu Zhou. Tencent Cloud Xiaowei Beijing.
[44]
Xu, W., Li, C., Lee, M., & Zhang, C. (2020). Multi-task learning for abstractive text summarization with key information guide network. EURASIP Journal on Advances in Signal Processing, 2020(1), 16.
Index Terms
- Ensemble of Support Vector Machine and Ontological Structures to Generate Abstractive Text Summarization
Index terms have been assigned to the content through auto-classification.
Recommendations
A framework for multi-document abstractive summarization based on semantic role labelling
We have proposed a framework for multi-document abstractive summarization based on semantic role labeling (SRL). To the best of our knowledge, SRL has not been employed for abstractive summarization.The integration of genetic algorithm with SRL based ...
An Ontology-Based Approach to Text Summarization
WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03Extractive text summarization aims to create a condensed version of one or more source documents by selecting the most informative sentences. Research in text summarization has therefore often focused on measures of the usefulness of sentences for a ...
Graph-based abstractive biomedical text summarization
Graphical abstractDisplay Omitted
Highlights- A graph generation and frequent itemset mining approach have been used for the generation of extractive summaries.
- The T5 model has been adopted to generate abstractive summaries in the biomedical domain.
- The ROUGE metric has been ...
AbstractSummarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. The first ...
Comments
Information & Contributors
Information
Published In
Copyright © 2022.
Publisher
IGI Global
United States
Publication History
Published: 23 August 2022
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Jan 2025
Other Metrics
Citations
Cited By
View all- Wu H(2024)Dilated convolution for enhanced extractive summarizationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23470946:2(4777-4790)Online publication date: 14-Feb-2024