Abstract
Text summarization gives condensed information of long texts and documents. Extractive and abstractive are two approaches for summarizing texts. Extractive text summarization is a type of forming summary by finding out the key phrases in a text or article. These important phrases are extracted to form the summary. In this paper, we aim to carry out text summarization of texts in two foreign languages, namely English and German, and two Indian languages: Marathi and Hindi. We have considered an entire sentence in a text as a feature to be extracted to form the summary. We have summarized news articles in the above-mentioned languages. For summarization, supervised or unsupervised algorithms can be used. We aim to find performance of unsupervised algorithms, namely weighted, TextRank, and fuzzy logic for summarizing texts in the above-mentioned languages. The performance evaluation of algorithms is done using ROUGE metric.
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 (2017) A survey on extractive text summarization, ICCCSP
Bhosale S, Joshi D, Bhise V, Deshmukh RA (2018) Marathi e-newspaper text summarization using automatic keyword extraction technique. Int J Adv Eng Res Dev 5(03)
Sherry, Saini A (2017) Multilingual text summarization techniques. IJRET 06(06)
Sri Rama Raju Bhargav Allarpu T (2017) Text summarization using sentence scoring method. IRJET 4(4)
Mihalcea R, Tarau P. TextRank: bringing order into texts. Department of Computer Science University of North Texas
Tanwi, Ghosh S, Kumar V, Jain YS, Avinash B (2014) Automatic text summarization using Text Rank. IRJET 6(4)
Pallavi, Patil D, Kulkarni NJ (2014) Text summarization system using Fuzzy Logic. IJIRAE 1(3)
Steinberger J, Jeˇzek K (2009) Evaluation measure for Text Summarization. Comput Informatics 28
Lin C-Y (2004) ROUGE: a package for automatic evaluation of summaries. ACL Anthology, Vol: Text summarization branches out
Verma P, Verma A (2020) Accountability of NLP tools in text summarization for Indian languages. J Sci Res 64(1)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Oka, T., Patankar, P., Rege, S., Dixit, M. (2022). Text Summarization of News Articles. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Lecture Notes in Networks and Systems, vol 321. Springer, Singapore. https://doi.org/10.1007/978-981-16-5987-4_44
Download citation
DOI: https://doi.org/10.1007/978-981-16-5987-4_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5986-7
Online ISBN: 978-981-16-5987-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)