Abstract
In recent times, we have abundant sources for information out of which not every detail is significant. So the point of concern is identifying the most relevant and important information out of vast data. The focus should be given to the relevant information present in the entire data in such a way that we can understand the entire gist of the text. Considering the above concern as our problem statement, in this paper we discuss “Text Summarization”—it is the process of generating pertinent and significant text from the entire information. Text resources include websites, text documents, and direct text feed from users. Text Summarization can be performed using 2 methods—Extractive Summarization and Abstractive summarization. Here we are going to generate a summary from various types of text sources using extractive techniques. In extractive techniques, we have used a frequency driven approach where the relevance of a particular sentence is measured through sentence weightage and term frequency. We measured the accuracy of the model using a sequence matcher which provides us the relevance between generated summary and human perceived summary.
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
Similar content being viewed by others
References
Moratanch, N., Chitrakala, S.: A survey on extractive text summarization. In: 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP) (2017)
Annapurna, V., Ramakrishna Murty, M. et al.: Comparative analysis of frequent pattern mining for large data using FP-tree and CP-tree methods. In: International Conference FICTA-17 at KIIT University, Bhubaneswar, Springer, AISC series (2017)
Madhuri, J. N., Ganesh Kumar, R.: Extractive text summarization using sentence ranking. In: 2019 International Conference on Data Science and Communication (IconDSC), pp. 1–3. Bangalore, India (2019). https://doi.org/10.1109/IconDSC.2019.8817040
Andhale, N., Bewoor, L. A.: An overview of text summarization techniques. In: 2016 International Conference on Computing Communication Control and automation (ICCUBEA), pp. 1-7. Pune (2016). https://doi.org/10.1109/ICCUBEA.2016.7860024
Patil, A.P., Dalmia, S., Ansari, S.A.A., Aul, T., Bhatnagar, V.: Automatic text summarizer. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1530–1534. New Delhi (2014). https://doi.org/10.1109/ICACCI.2014.6968629
Ganiger, S., Rajashekharaiah, K.M.M.: Comparative study on keyword extraction algorithms for single extractive document. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE (2018)
Krishnaveni, P., Balasundaram, S.R.: Automatic text summarization by local scoring and ranking for improving coherence. In: 2017 International Conference on Computing Methodologies and Communication (ICCMC) (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), India, May 19–20, 2017
Mishra, A.R., Panchal, V.K., Kumar, P.: Extractive text summarization—an effective way to extract information from text. In: International Conference on contemporary Computing and Informatics (IC3I). IEEE (2019)
Afsharizadeh, M., Ebrahimpour-Komleh, H., Bagheri, A.: Query-oriented text summarization using sentence extraction technique. In: 2018 4th International Conference on Web Research (ICWR) (2018)
Rahimi, S.R., Mozhdehi, A.T.: An overview of extractive text summarization. In: IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI) Dec. 22, 2017 (Iran University of Science and Technology), Tehran, Iran
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mohan Kalyan, V., Santhaiah, C., Naga Sri Nikhil, M., Jithendra, J., Deepthi, Y., Krishna Rao, N.V. (2021). Extractive Summarization Using Frequency Driven Approach. In: Mai, C.K., Reddy, A.B., Raju, K.S. (eds) Machine Learning Technologies and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4046-6_18
Download citation
DOI: https://doi.org/10.1007/978-981-33-4046-6_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4045-9
Online ISBN: 978-981-33-4046-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)