A Short Survey of Automatic Text Summarization Techniques, Algorithms and Their Evaluation
Keywords:
Automatic Text Summarization, Extractive Summarization, Abstractive Summarization, Text Mining, Natural Language ProcessingAbstract
Automatic text summarization has become an indispensable tool in the digital era, empowering users to efficiently extract and distill key information from vast troves of textual data. This comprehensive survey paper delves deep into the diverse techniques and algorithms employed in the field of automatic text summarization. It explores the core methods, including both extractive and abstractive approaches, as well as the underlying algorithms and techniques that power these summarization systems. The paper delves into the capabilities and limitations of these summarization methods, discussing their real-world applications in depth. It also examines the current state-of-the-art in automatic text summarization research, highlighting the notable advancements and innovations that are pushing the boundaries of this dynamic field. The survey provides a thorough and insightful analysis, equipping readers with a nuanced understanding of the key trends, challenges, and future directions in the realm of automatic text summarization.
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K. Thakkar, R. V. Dharaskar, and M. Chandak, “Graph-Based Algorithms for Text Summarization,” Nov. 01, 2010. doi: 10.1109/icetet.2010.104.
V. Gupta and G. S. Lehal, “A Survey of Text Summarization Extractive Techniques,” Aug. 20, 2010. doi: 10.4304/jetwi.2.3.258-268.
A. M. Rush, S. Chopra, and J. Weston, “A Neural Attention Model for Abstractive Sentence Summarization,” Jan. 01, 2015, Cornell University. doi: 10.48550/arxiv.1509.00685.
Gevorg Poghosyan, gevorg.poghosyan@insight-centre.org, “Addressing Information Overload through Text Mining across News and Social Media Streams.” Sep. 2019. [Available: https://dl.acm.org/doi/10.1145/3345645.3351105
X. Wu, F. Xie, G. Wu, and W. Ding, “PNFS: PERSONALIZED WEB NEWS FILTERING AND SUMMARIZATION,” Oct. 01, 2013, World Scientific. doi: 10.1142/s0218213013600075.
S. Saiyed and S. S. Priti, “Literature Review on Extractive Text Summarization Approaches,” Dec. 15, 2016. doi: 10.5120/ijca2016912574.
E. Greussing and H. G. Boomgaarden, “Shifting the refugee narrative? An automated frame analysis of Europe’s 2015 refugee crisis,” Feb. 01, 2017, Taylor & Francis. doi: 10.1080/1369183x.2017.1282813.
A. K. Singh, M. Gupta, and V. Varma, “Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization,” Jan. 01, 2019, Cornell University. doi: 10.48550/arxiv.1912.11688.
A. Singh, M. Gupta, and V. Varma, “Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization,” Apr. 27, 2018, Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v32i1.11994.
P. C. R. Raj, A. Bhandari, A. Singh, M. Puri, and S. Malik, “Comparison of Matrix Factorization and Graph-Based Models for Summary Extraction,” Mar. 01, 2019. [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8991343
C. Mallick, A. K. Das, M. Dutta, A. K. Das, and A. Sarkar, “Graph-Based Text Summarization Using Modified TextRank,” in Advances in intelligent systems and computing, Springer Nature, 2018, p. 137. doi: 10.1007/978-981-13-0514-6_14.
K. Filippova and M. Strube, “Sentence fusion via dependency graph compression,” Jan. 01, 2008. doi: 10.3115/1613715.1613741.
J. Zhou and A. M. Rush, “Simple Unsupervised Summarization by Contextual Matching,” Jan. 01, 2019. doi: 10.18653/v1/p19-1503.
Abhishek Kumar Singh,Manish Gupta,Vasudeva Varma, “Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization.” Dec. 2019. Available: https://arxiv.org/pdf/1912.11688v1.pdf
A. M. Rush, H. Seas, S. Chopra, and J. Weston, “A Neural Attention Model for Sentence Summarization,” Jan. 01, 2015.
Z. Cao, F. Wei, S. Li, W. Li, M. Zhou, and H. Wang, “Learning Summary Prior Representation for Extractive Summarization,” Jan. 01, 2015. doi: 10.3115/v1/p15-2136.
K. V. Kumar, D. Yadav, and A. Sharma, “Graph Based Technique for Hindi Text Summarization,” in Advances in intelligent systems and computing, Springer Nature, 2015, p. 301. doi: 10.1007/978-81-322-2250-7_29.
R. M. Aliguliyev, “A new sentence similarity measure and sentence based extractive technique for automatic text summarization,” Dec. 02, 2008, Elsevier BV. doi: 10.1016/j.eswa.2008.11.022.
A. Sakhadeo and N. Srivastava, “Effective extractive summarization using frequency-filtered entity relationship graphs,” Jan. 01, 2018, Cornell University. doi: 10.48550/arxiv.1810.10419.
P. Ren, F. Wei, Z. Chen, J. Ma, and M. Zhou, “A Redundancy-Aware Sentence Regression Framework for Extractive Summarization,” Dec. 01, 2016. Available: https://www.aclweb.org/anthology/C16-1004.pdf
R. Paulus, C. Xiong, and R. Socher, “A Deep Reinforced Model for Abstractive Summarization,” Jan. 01, 2017, Cornell University. doi: 10.48550/arxiv.1705.04304.
S. Esmaeilzadeh, G. X. Peh, and A. Xu, “Neural Abstractive Text Summarization and Fake News Detection,” Jan. 01, 2019, Cornell University. doi: 10.48550/arxiv.1904.00788.
A. See, P. J. Liu, and C. D. Manning, “Get To The Point: Summarization with Pointer-Generator Networks,” Jan. 01, 2017. doi: 10.18653/v1/p17-1099.
D. Galanis, Γ. Λάμπουρας, and I. Androutsopoulos, “Extractive Multi-Document Summarization with Integer Linear Programming and Support Vector Regression,” Dec. 01, 2012. Available: http://nlp.cs.aueb.gr/pubs/coling2012.pdf
Abhishek Kumar Singh, abhishek.singh@research.iiit.ac.in, Manish Gupta, manish.gupta@iiit.ac.in, Vasudeva Varma, “Hybrid MemNet for Extractive Summarization.” Nov. 2017. Available: https://dl.acm.org/doi/10.1145/3132847.3133127
A. Bharadwaj, A. Srinivasan, A. Kasi, and B. Das, “Extending The Performance of Extractive Text Summarization By Ensemble Techniques,” Dec. 01, 2019. doi: 10.1109/icoac48765.2019.246854.
A. Singh, M. Gupta, and V. Varma, “Hybrid MemNet for Extractive Summarization,” Nov. 06, 2017. doi: 10.1145/3132847.3133127.
R. D. Gaudio, A. Burchardt, and A. Lommel, “Evaluating a Machine Translation System in a Technical Support Scenario,” Jan. 01, 2015. Available: https://www.aclweb.org/anthology/W15-5705/
G. Penn and X. Zhu, “A Critical Reassessment of Evaluation Baselines for Speech Summarization,” Jun. 01, 2008. [Online]. Available: https://aclanthology.org/P08-1054/
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