Graph-Based Extractive Text Summarization Models: A Systematic Review

 American Library Association, Office of Intellectual Freedom. (2013). Banned and challenged books. Retrieved September 15, 2015, from http://www.ala.org/advocacy/banned/bannedbooksweek
Abualigah, L., Bashabsheh, M. Q., Alabool, H., & Shehab, M. (2020). Text summarization: A brief review. Stud. Comput. Intell., 874, 1-15. 
Aker, A. (2013). Entity type modeling for multi-document summarization: generating descriptive summaries of geo-located entities. A thesis submitted in fulfilment of requirements for the degree of Doctor of Philosophy to Department of Computer Science University of Sheffield. 
Al-Khassawneh, Y. A., Salim, N., & Jarrah, M. (2017). Improving triangle-graph based text summarization using hybrid similarity function. Indian Journal of Science and Technology, 10(8). doi:10.17485.
Alami, N., El Adlouni, Y., En-Nahnahi, N., & Meknassi, M. (2018) Using statistical and semantic analysis for Arabic text summarization. In: Vol. 640. Advances in Intelligent Systems and Computing (pp. 35-50).
Altmami, N. I., & Menai, M. E. B. (2018b) Semantic graph based automatic summarization of multiple related work sections of scientific articles. In: Vol. 11089 LNAI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 255-259).
Altmami, N. I., & Menai, M. E. B. (2020). Automatic summarization of scientific articles: A survey. Journal of King Saud University – Computer and Information Sciences 
AlZahir, S., Fatima, Q., & Cenek, M. (2015). New graph-based text summarization method. Paper presented at the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM).
Alzuhair, A., & Al-Dhelaan, M. (2019). An approach for combining multiple weighting schemes and ranking methods in graph-based multi-document summarization. IEEE Access, 7, 120375-120386. 
Aries, A., Zegour, D. E., & Hidouci, W. K. (2019). Automatic text summarization: What has been done and what has to be done. arXiv:1904.00688v1 [cs.CL] 1. 
Barrios, F., López, F., Argerich, L., & Wachenchauzer, R. (2016). Variations of the similarity function of textrank for automated summarization. arXiv:1602.03606 [cs.CL], 65-72. 
Baxendale, P. B. (1958). Machine-made index for technical literature: an experiment. IBM J. Res. Dev., 2(4). 
Begum, N., Fattah, M., & Ren, F. (2009). Automatic text summarization using support vector machine. International Journal of Innovative Computing, Information & Control: IJICIC, 5(7), 1987-1996. 
Bhattacharya, P., Hiware, K., Rajgaria, S., Pochhi, N., Ghosh, K., & Ghosh, S. (2019). A comparative study of summarization algorithms applied to legal case judgments. paper presented at the advances in information retrieval, cham. 
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30, 1-7. 
Cai, X., & Li, W. (2013). Ranking through clustering: an integrated approach to multi-document summarization. IEEE Transactions on Audio Speech & Language Processing, 21(7), 1424-1433. 
Carbonell, J., & Goldstein, J. (1998). The use of mmr, diversity-based reranking for reordering documents and producing summaries. paper presented at the 21st annual international acm sigir conference on research and development in information retrieval.
Dalal, V., & Malik, L. (2018) Semantic graph based automatic text summarization for hindi documents using particle swarm optimization. in: vol. 84 (pp. 284-289): springer science and business media deutschland gmbh.
Dutta, S., Chandra, V., Mehra, K., Ghatak, S., Das, A. K., & Ghosh, S. (2019). Summarizing microblogs during emergency events: A comparison of extractive summarization algorithms. paper presented at the international conference on emerging technologies in data mining and information security (iemis 2018), kolkata, india. 
Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the ACM, 16(2), 264–285. 
El-Kassas, W. S., Salama, C. R., Rafea, A. A., & Mohamed, H. K. (2020). EdgeSumm: Graph-based framework for automatic text summarization. Information Processing and Management, 57(6). doi: 10.1016/j.ipm.2020.102264
El-Kassas, W. S., Salama, C. R., Rafea, A. A., & Mohamed, H. K. (2021). Automatic text summarization: A comprehensive survey. Expert Systems with Applications. 
Elbarougy, R., Behery, G., & Khatib, A. E. (2019). Extractive Arabic text summarization using modified PageRank algorithm. Egyptian informatics journal. 
Elrefaiy, A., Abas, A. R., & Elhenawy, I. (2018). Review of recent techniques for extractive text summarization. Journal of Theoretical and Applied. Information Technology, 96, 7739–7759. 
Erkan, G., & Radev, D. R. (2004). LexRank: Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research, 22, 457-479. 
Fakhrezi, M. F., Bijaksana, M. A., & Huda, A. F. (2021). Implementation of automatic text summarization with TextRank method in the development of al-qur'an vocabulary encyclopedia. 5th international conference on computer science and computational intelligence 2020, 179, 391–398. 
Fang, C., Mu, D., Deng, Z., & Wu, Z. (2017). Word-sentence co-ranking for automatic extractive text summarization. Expert Systems with Applications, 72, 189-195. doi: 10.1016/j.eswa.2016.12.021
Gallo, M., Popelínský, L., & Vaculík, K. (2018). To text summarization by dynamic graph mining. CEUR Workshop Proceedings, 2203, 28–34. 
Gambhir, M., & Gupta, V. (2017). Recent automatic text summarization techniques: A survey. Artificial Intelligence Review, 47(1), 1-66. doi: https://doi.org/10.1007/s10462-016-9475-9
Gong, Y., & Liu, X. (2001). Generic text summarization using relevance measure and latent semantic analysis. paper presented at the annual international acm sigir conference on research and development in information retrieval.
Gunawan, D., Pasaribu, A., Rahmat, R. F., & Budiarto, R. (2017). Automatic text summarization for Indonesian language using textteaser. paper presented at the iop conference series: materials science and engineering.
Gupta, S., & Gupta, S. K. (2019). Abstractive summarization: An overview of the state of the Art. Expert System and Application, 121, 49–65. 
Han, X., Lv, T., Hu, Z., Wang, X., & Wang, C. (2016). Text Summarization Using FrameNet-Based Semantic Graph Model Hindawi Publishing Corporation Scientific Programming. 
Hark, C., & Karcı, A. (2020). Karcı summarization: A simple and effective approach for automatic text summarization using Karcı entropy. Information Processing and Management, 57(3). doi: 10.1016/j.ipm.2019.102187
Hassan, B., Abdelrahman, S. E., Bahgat, R., & Farag, I. (2019). UESTS: An unsupervised ensemble semantic textual similarity method. IEEE Access. 
Herings, P. J., Laan, G. V. d., & Talman, D. (2001). Measuring the power of nodes in digraphs. Technical report, Tinbergen Institute. 
Hou, L., Hu, P., & Bei, C. (2017). Abstractive document summarization via neural model with joint attention. Paper presented at the Natural Language Processing and Chinese Computing, Dalian, China. 
Hou, S., & Lu, R. (2020). Knowledge-guided unsupervised rhetorical parsing for text summarization. Information Systems. 
Hu, P., He, J., & Zhang, Y. (2015). Graph-based query-focused multi-document summarization using improved affinity graph. In W. M. Zhang S., Zhang Z. (eds) (Ed.), knowledge science, engineering and management. ksem 2015. lecture notes in computer science (Vol. 9403): Springer, Cham.
Jacquenet, F., Bernard, M., & Largeron, C. (2019). Meeting summarization, a challenge for deep learning. Paper presented at the advances in computational intelligence, Cham. 
Kanitha, D. K., Mubarak, D. M. N., & Shanavas, S. A. (2018). Malayalam text summarization using graph-based method. International Journal of Computer Science and Information Technologies, 9(2), 40-44. 
Khan, A., Salim, N., Farman, H., Khan, M., Jan, B., Ahmad, A., & Paul, A. (2018). Abstractive text summarization based on improved semantic graph approach. International Journal of Parallel Programming, 46(5), 992–1016. doi: https://doi.org/ 10.1007/s10766-018-0560-3
Kleinberg, J. M. (1999). Authoritative sources in a hyper linked environment. Journal of the ACM, 46(5), 604-632. 
Kumar, A., & Sharma, A. (2019). Systematic literature review of fuzzy logic-based text summarization. Iranian journal of fuzzy systems, 16(5), 45-59. 
Lierde, H. V., & Chow, T. W. S. (2019). Query-oriented text summarization based on hypergraph transversals. Information processing and management, 56. 
Lin, H., & Ng, V. (2019). Abstractive summarization: a survey of the state of the art. Paper presented at the thirty-third aaai conference on artificial intelligence (AAAI-19). 
Liu, Y., Safavi, T., Dighe, A., & Koutra, D. (2018). Graph summarization methods and applications: A survey. ACM computing surveys (CSUR), 51(3). 
Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM journal of research and development, 159–165. 
Mallick, C., Das, A. K., Dutta, M., Das, A. K., & Sarkar, A. (2018). Graph-based text summarization using modified TextRank. paper presented at the soft computing in data analytics, advances in intelligent systems and computing. 
Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing order into texts. In proceedings of the conference on empirical methods in natural language processing, 404-411. 
Miranda-Jiménez, S., Gelbukh, A., & Sidorov, G. (2013). Summarizing conceptual graphs for automatic summarization task. In H. D. Pfeiffer, D. I. Ignatov, J. Poelmans, & N. Gadiraju (Eds.), conceptual structures for stem research and education. Iccs 2013. Lecture notes in computer science (Vol. 7735). Berlin, Heidelberg: Springer.
Mohamed, M., & Oussalah, M. (2019). SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis. Information processing and management, 56(4), 1356-1372. doi: 10.1016/j.ipm.2019.04.003
Mojrian, M., & Mirroshandel, S. A. (2021). A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA. Expert systems with applications, 171. 
Moradi, M. (2018). Frequent item sets as meaningful events in graphs for summarizing biomedical texts. Paper presented at the 8th international conference on computer and knowledge engineering (ICCKE), 2018.
Moradi, M., Dashti, M., & Samwald, M. (2020). Summarization of biomedical articles using domain-specific word embeddings and graph ranking. Journal of Biomedical Informatics, 107. 
Moratanch, N., & Chitrakala, S. (2017). A survey on extractive text summarization. Paper presented at the 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), Chennai. 
Mosa, M. A., Anwar, A. S., & Hamouda, A. (2019). A survey of multiple types of text summarization with their satellite contents based on swarm intelligence optimization algorithms. Knowledge-Based Systems, 163, 518–532. 
Mussina, A., Aubakirov, S., & Trigo, P. (2018). Automatic document summarization based on statistical information. Paper presented at the 7th International Conference on Data Science, Technology and Applications (DATA 2018).
Narayan, S., Cohen, S. B., & Lapata, M. (2018). Ranking sentences for extractive summarization with reinforcement learning. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 1747–1759. 
Narayan, S., Cohen, S. B., & Lapata, M. (2019). What is this article about? Extreme summarization with topic-aware convolutional neural networks. Journal of Artificial Intelligence Research, 66, 243-278. 
Nasar, Z., Jaffry, S. W., & Malik, M. K. (2019). Textual keyword extraction and summarization: State-of-the-art. Information Processing & Management, 56(6). 
Natesh, A. A., Balekuttira, S. T., & Patil, A. P. (2016). Graph based approach for automatic text summarization. International Journal of Advanced Research in Computer and Communication Engineering, 5(2), 6-9. 
Nawaz, A., Bakhtyar, M., Baber, J., Ullah, I., Noor, W., & Basit, A. (2020). Extractive text summarization models for Urdu language. Information Processing and Management. 
Nazari, N., & Mahdavi, M. A. (2019). A survey on automatic text summarization. Journal of AI and Data Mining, 7, 121-135. 
Patil, K., & Brazdil, P. (2007). Text summarization: using centrality in the pathfinder network. Paper presented at the IADIS International Conference Applied Computing. 
Plaza, L., & Díaz, A. (2011). Using semantic graphs and word sense disambiguation techniques to improve text summarization. Procesamiento de Lenguaje Natural, 47, 97-105. 
Plaza, L., Díaz, A., & Gervás, P. (2011). A semantic graph-based approach to biomedical summarization. Artificial Intelligence in Medicine, 53(1), 1-14. doi: 10.1016/j.artmed.2011.06.005
Saziyabegum, S., & Sajja, P. S. (2016). Literature review on extractive text summarization approaches. International Journal of Computer Applications (0975 – 8887), 156-No12. 
Saziyabegum, S., & Sajja, P. S. (2017). Review on text summarization evaluation methods. Indian Journal of Computer Science and Engineering (IJCSE), 8 No. 4. 
Sevilla, A. F. G., Fernández-Isabel, A., & Díaz, A. (2016) Enriched semantic graphs for extractive text summarization. In: Vol. 9868 LNAI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 217-226).
Sikder, R., Hossain, M. M., & Robi, F. M. R. H. (2019). Automatic text summarization for Bengali language including grammatical analysis. International Journal of Scientific & Technology Research, 8(6). 
Suleiman, D., & Awajan, A. A. (2019). Deep learning based extractive text summarization: Approaches, datasets and evaluation measures. Paper presented at the 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). 
Tandel, J., Mistree, K., & Shah, P. (2019). A review on neural network based abstractive text summarization models. Paper presented at the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT. 
Uçkan, T., & Karcı, A. (2020). Extractive multi-document text summarization based on graph independent sets. Egyptian Informatics Journal. 
Ullah, S., & Al Islam, A. B. M. A. (2019). A framework for extractive text summarization using semantic graph-based approach. Paper presented at the ACM International Conference Proceeding Series.
Verma, P., & Verma, A. (2020). A review on text summarization techniques. Journal of Scientific Research, 64(1). 
Vollmer, M., Golab, L., Böhm, K., & Srivastava, D. (2019). Informative summarization of numeric data. Paper presented at the 31st International Conference on Scientific and Statistical Database Management (SSDBM ’19), Santa Cruz, CA, USA. 
Wan, X., & Yang, J. (2006). Improved affinity graph based multi-document summarization. Paper presented at the Human Language Technology Conference of NAACL.
Wang, K., Liu, T., Sui, Z., & Chang, B. (2017). Affinity-Preserving Random Walk for Multi-Document Summarization. Paper presented at the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.
Wang, S., Zhao, X., Li, B., Ge, B., & Tang, D. (2017). Integrating Extractive and Abstractive Models for Long Text Summarization. Paper presented at the Proceedings - 2017 IEEE 6th International Congress on Big Data, Big Data Congress 2017.
Wang, W., Li, S., Li, J., Li, W., & Wei, F. (2013). Exploring hypergraph-based semi-supervised ranking for query-oriented summarization. Information Sciences, 237, 271-286. 
Wang, W., Wei, F., Li, W., & Li, S. (2009). Hypersum: hypergraph based semi-supervised sentence ranking for query-oriented summarization. Paper presented at the 18th ACM conference on information and knowledge management.
Widyassari, A. P., Rustad, S., Shidik, G. F., Noersasongko, E., Abdul Syukur a, Affandy, A., & Setiadi, D. R. I. M. (2020). Review of automatic text summarization techniques & methods. Journal of King Saud University – Computer and Information Sciences. 
Woloszyn, V., Machado, G. M., Wives, L. K., & Mo, J. e. P. (2018). Modeling comprehending and summarizing textual content by graphs. arXiv:1807.00303v1 [cs.CL]. 
Xiong, S., & Ji, D. (2016). Query-focused multi-document summarization using hypergraph-based ranking. An International Journal of Information Processing and Management, 52(4). 
Yao, J.-g., Wan, X., & Xiao, J. (2017). Recent advances in document summarization. Knowledge & Information Systems, 1-40. 
Zamana, F., Shardlow, M., Hassan, S.-U., Aljohani, N. R., & Nawaz, R. (2020). HTSS: A novel hybrid text summarization and simplification architecture. Information Processing and Management. 
Zhong, S.-h., Liu, Y., Li, B., & Long, J. (2015). Query-oriented unsupervised multi-document summarization via deep learning model. Expert Systems with Applications, 8146 -8155. 
Ziheng, L. (2007). Graph-based methods for automatic text summarization. (Ph.D.), National University of Singapore,