Abstract
Text summarization is the technique of extricating notable data from the first content archive. In this procedure, the separated data is produced as a consolidated report and introduced as a clearly expressed rundown. Extractive summarization technique includes choosing critical text or themes from the content and compiling it into a shorter frame. The significance of sentences is chosen in the light of measurable and semantic highlights of sentences. The proposed paper delineates the procedure for text summarization using the Maximal Marginal Relevance (MMR) scoring methodology. The system identifies the most relevant words and selects the sentences, which are similar to the query generated by the words. This automated text summarization algorithm is capable of re-ranking the sentences from the archive, while taking into consideration the semantics and producing a shorter content capable of representing the original content.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Moratanch, N., Chitrakala, S.: A survey on abstractive text summarization. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–7, Mar 2016. IEEE
Barzilay, R., McKeown, K.R.: Sentence fusion for multidocument news summarization. Comput. Linguist. 31(3), 297–328 (2005)
Lee, C.S., Jian, Z.W., Huang, L.K.: A fuzzy ontology and its application to news summarization. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics), 35(5), 859–880 (2005)
John, A., Wilscy, M.: Random forest classifier based multi-document summarization system. In: 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 31–36, Dec 2013. IEEE
Ganesan, K., Zhai, C., Han, J.: Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 340–348, Aug 2010. Association for Computational Linguistics
Lloret, E., Palomar, M.: Analyzing the use of word graphs for abstractive text summarization. In: Proceedings of the First International Conference on Advances in Information Mining and Management, Barcelona, Spain, pp. 61–66 (2011)
Barrios, F., López, F., Argerich, L., Wachenchauzer, R.: Variations of the similarity function of textrank for automated summarization (2016). arXiv:1602.03606
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, June 2011. Association for Computational Linguistics
Khan, A., Salim, N., Kumar, Y.J.: A framework for multi-document abstractive summarization based on semantic role labelling. Appl. Soft Comput. 30, 737–747 (2015)
Gupta, V., Lehal, G.S.: A survey of text summarization extractive techniques. J. Emerg. Technol. Web Intell. 2(3), 258–268 (2010)
Zhang, J., Sun, L., Zhou, Q.: A cue-based hub-authority approach for multi-document text summarization. In: Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE’05, pp. 642–645. IEEE (2005)
Ferreira, R., Freitas, F., de Souza Cabral, L., Lins, R.D., Lima, R., França, G., Favaro, L.: A context based text summarization system. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 66–70, Apr 2014. IEEE
Zhang, P.Y., Li, C.H.: Automatic text summarization based on sentences clustering and extraction. In: 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009, pp. 167–170. IEEE, Aug 2009
Nallapati, R., Zhou, B., Ma, M.: Classify or select: neural architectures for extractive document summarization (2016). arXiv:1611.04244
Narayan, S., Papasarantopoulos, N., Lapata, M., Cohen, S.B.: Neural extractive summarization with side information (2017). arXiv:1704.04530
Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133–142, Dec 2003
Xie, S., Liu, Y.: Using corpus and knowledge-based similarity measure in maximum marginal relevance for meeting summarization. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp. 4985–4988. IEEE, Mar 2008
Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336, Aug 1998. ACM
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E.D., Gutierrez, J.B., Kochut, K.: Text summarization techniques: a brief survey (2017). arXiv:1707.02268
Acknowledgements
The system was developed by students as a part of final year project. The project was conceivable due to guidance from the project mentor, Dr. Deepak Sharma. The infrastructure provided by the college, K. J. Somaiya College of Engineering, India, helped in the successful development of the project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mahajani, A., Pandya, V., Maria, I., Sharma, D. (2019). Ranking-Based Sentence Retrieval for Text Summarization. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 851. Springer, Singapore. https://doi.org/10.1007/978-981-13-2414-7_43
Download citation
DOI: https://doi.org/10.1007/978-981-13-2414-7_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2413-0
Online ISBN: 978-981-13-2414-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)