Abstract
Automatic text summarization has very efficient techniques to create the summarization (important information form document) of the text document. As we know the information on the internet is growing rapidly and we cannot go through all the documents on the internet. If we want to read the news we have to identify which news we have to read then the news article can be summarized in only one paragraph by information extraction from the news article and by reading this short paragraph we can identify which article we should read. The automatic text summarization can be done by three methods. First one is the extraction method in which we just find the informative sentences from the whole document text. The second approach is the abstraction technique in which we find the meaning of the sentences and try to formulate the paragraph in the same meaning manner or we can say the same thing express the fewer words. The third and last approach is compression technique in which compress each sentence of the document. We compress the sentence by having the words related to the meaning of the sentence by title or first sentence of the graph. We can apply the hybrid approach to create the summarization in which we first compress the whole text document and then apply knowledge extraction. Knowledge extraction can be done by giving the score to each sentence and then pick the best k sentences according to compression ratio.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhi, Z., Hin, H.K.P., Gay, R.K.L., Lin, G.W., Yang, L.S.: Itsum: one agent-based system for automated text summarizing. In: 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No. 01EX479), vol. 3, pp. 18–25. IEEE (2001)
Edmundson, H.P., Oswald, V.A.: Automatic indexing and abstracting of the contents of documents. Planning Research Corporation (1959)
Pinto, M.: Engineering the production of meta-information: the abstracting con-cern. J. Inf. Sci. 29(5), 405–417 (2003)
Li, H., Zhu, J., Ma, C., Zhang, J., Zong, C.: Read, watch, listen and summarize: multi-modal summarization for asynchronous text, image, audio and video. IEEE Trans. Knowl. Data Eng. 31(5), 996–1009 (2018)
Afsharizadeh, M., Ebrahimpour-Komleh, H., Bagheri, A.: Query-oriented text summarization using sentence extraction technique. In: 2018 4th International Conference on Web Research (ICWR), pp. 128–132. IEEE (2018)
Sethi, P., Sonawane, S., Khanwalker, S., Keskar, R.: Automatic text summa-rization of news articles. In: 2017 International Conference on Big Data, IoT and Data Science (BID), pp. 23–29. IEEE (2017)
Wan, L.: Extraction algorithm of english text summarization for english teaching. In: 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 307–310. IEEE (2018)
Rahimi, S.R., Mozhdehi, A.T., Abdolahi, M.: An overview on extractive text summarization. In: 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp. 0054–0062. IEEE (2017)
Jain, A., Bhatia, D., Thakur, M.K.: Extractive text summarization using word vector embedding. In: 2017 International Conference on Machine Learning and Data Science (MLDS), pp. 51–55. IEEE (2017)
Zhang, C., Sah, S., Nguyen, T., Peri, D., Loui, A., Salvaggio, C., Ptucha, R.: Semantic sentence embeddings for paraphrasing and text summarization. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 705–709. IEEE (2017)
Rai, S., Gaikwad, T., Jain, S., Gupta, A.: Method level text summarization for java code using nano-patterns. In: 2017 24th Asia-Pacific Software Engineering Conference (APSEC), pp. 199–208. IEEE (2017)
Krishnaveni, P., Balasundaram, S.: Automatic text summarization by local scoring and ranking for improving coherence. In: 2017 International Conference on Computing Methodologies and Communication (ICCMC), pp. 59–64. IEEE (2017)
Gupta, P., Tiwari, R., Robert, N.: Sentiment analysis and text summarization of online reviews: a survey. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 0241–0245. IEEE (2016)
Indu, M., Kavitha, K.: Review on text summarization evaluation methods. In: 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS), pp. 1–4. IEEE (2016)
Moratanch, N., Chitrakala, S.: A survey on extractive text summarization. In: International Conference on Computer, Communication and Signal Processing (ICCCSP), pp. 1–6. IEEE (2017)
Yadav, N., Chatterjee, N.: Text summarization using sentiment analysis for duc data. In: 2016 International Conference on Information Technology (ICIT), pp. 229–234. IEEE (2016)
Kumar, A., Sharma, A., Sharma, S., Kashyap, S.: Performance analysis of keyword extraction algorithms assessing extractive text summarization. In: International Conference on Computer, Communications and Electronics (Comptelix), pp. 408–414. IEEE (2017)
Mirani, T.B., Sasi, S.: Two-level text summarization from online news sources with sentiment analysis. In: 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), pp. 19–24. IEEE (2017)
Naik, S.S., Gaonkar, M.N.: Extractive text summarization by feature-based sentence extraction using rule-based concept. In: 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1364–1368. IEEE (2017)
Shetty, K., Kallimani, J.S.: Automatic extractive text summarization using k-means clustering. In: 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 1–9. IEEE (2017)
Moratanch, N., Chitrakala, S.: A survey on abstractive text summarization. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–7. IEEE (2016)
Meena, Y.K., Dewaliya, P., Gopalani, D.: Optimal features set for extractive automatic text summarization. In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies, pp. 35–40. IEEE (2015)
Pal, A.R., Saha, D.: An approach to automatic text summarization using wordnet. In: 2014 IEEE International Advance Computing Conference (IACC), pp. 1169–1173. IEEE (2014)
Chandra, M., Gupta, V., Paul, S.K.: A statistical approach for automatic text summarization by extraction. In: 2011 International Conference on Communication Systems and Network Technologies, pp. 268–271. IEEE (2011)
Singh, A., Dey, N., Ashour, A.S., Santhi, V.: Web semantics for textual and visual information retrieval. In: IGI Global (2017)
Karaa, W.B.A., Ashour, A.S., Sassi, D.B., Roy, P., Kausar, N., Dey, N.: Med-line text mining: an enhancement genetic algorithm based approach for document clustering. In: Applications of Intelligent Optimization in Biology and Medicine, pp. 267–287. Springer (2016)
Wlodarczak, P., Ally, M., Soar, J.: Data process and analysis technologies of big data. In: Networking for Big Data, pp. 103–19. Chapman and Hall/CRC (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kharita, M.K., Singh, P. (2020). Automatic Text Summarization Techniques Used in Industry. In: Singh, P., Panigrahi, B., Suryadevara, N., Sharma, S., Singh, A. (eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-030-30577-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-30577-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30576-5
Online ISBN: 978-3-030-30577-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)