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Automatic Key-Phrase Extraction: Empirical Study of Graph-Based Methods

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Proceedings of the 5th International Conference on Big Data and Internet of Things (BDIoT 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 489))

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Abstract

Key-phrases in a document are phrases that provide a high-level description of its content without reading it completely. In some research articles, authors specify key-phrases in the articles they have written. However, the vast majority of books, articles, and web pages published every day, lack key-phrases. The manual extraction of these phrases is a tedious task and takes a long time. For this reason, automatic key-phrase extraction (AKE), which is an area of Text Mining, remains the best solution to overcome these difficulties. Because they are used in many Natural Language Processing (NLP) applications, such as text summarization and text classification. This article presents a comparison of some methods of extracting key-phrases from documents. Especially the graph-based approaches. These approaches are evaluated by their abilities to extract key-phrases. Our work focuses on the study of the performance of these methods in extracting key-phrases, whether from short or long texts, with the aim of providing information that contributes to improving their efficiency.

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References

  1. Aristovnik, A., Ravšelj, D., Umek, L.. A bibliometric analysis of COVID-19 across science and social science research landscape (2020)

    Google Scholar 

  2. Nasar, Z., Jaffry, S.W., Malik, M.K.: Textual keyword extraction and summarization: State-of-the-art. Inf. Process. Manage. 56(6), 102088 (2019)

    Article  Google Scholar 

  3. Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. ACM SIGIR Forum, 268–276 (2017)

    Google Scholar 

  4. Vallez, M., Pedraza-Jiménez, R., Codina, L., Blanco, S., Rovira, C.: A semi-automatic indexing system based on embedded information in html documents. Libr. Hi Tech 33(2), 195–210 (2015)

    Article  Google Scholar 

  5. Reilly, R.G., Sharkey, N.: Connectionist Approaches to Natural Language Processing. Routledge, Abingdon (2016)

    Book  Google Scholar 

  6. Sterckx, L., Demeester, T., Deleu, J., Develder, C.: Creation and evaluation of large key-phrase extraction collections with multiple opinions. Lang. Resour. Eval. 52(2), 503–532 (2018)

    Article  Google Scholar 

  7. Idrissi, N., Zellou, A.: A systematic literature review of sparsity issues in recommender systems. Soc. Netw. Anal. Min. 10(1), 15 (2020)

    Article  Google Scholar 

  8. Merrouni, Z.A., Frikh, B., Ouhbi, B.: Automatic key-phrase extraction: An overview of the state of the art. In: Proceedings of the 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 306–313. IEEE (2016)

    Google Scholar 

  9. Vijayarani, S., Ilamathi, M.J., Nithya, M.: Preprocessing techniques for text mining-an overview. Int. J. Comput. Sci. Commun. Netw. 5(1), 7–16 (2015)

    Google Scholar 

  10. Vijayarani, S., Janani, R., et al.: Text mining: Open source tokenization tools-an analysis. Adv. Comput. Intell. Int. J. (ACII) 3(1), 37–47 (2016)

    Google Scholar 

  11. Kaur, J., Buttar, P.K.: A systematic review on stopword removal algorithms. Int. J. Futur. Revolut. Comput. Sci. Commun. Eng. 4(4), 207–210 (2018)

    Google Scholar 

  12. Jivani, A.G.: A comparative study of stemming algorithms. Int. J. Comp. Tech. Appl. 2(6), 1930–1938 (2011)

    Google Scholar 

  13. Sarkar, K.: A hybrid approach to extract key-phrases from medical documents (2013). arXiv preprint arXiv:1303.1441

  14. Rafiei-Asl, J., Nickabadi, A.: Tsake: A topical and structural automatic key-phrase extractor. Appl. Soft Comput. 58, 620–630 (2017)

    Article  Google Scholar 

  15. Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. Int. J. Artif. Intell. Tools 13(01), 157–169 (2004)

    Article  Google Scholar 

  16. Papagiannopoulou, E., Tsoumakas, G.: A review of key-phrase extraction. Wiley Interdisc. Rev. Data Min. Knowl. Discovery 10(2), e1339 (2020)

    Google Scholar 

  17. Meng, R., Zhao, S., Han, S., He, D.: Deep key-phrase generation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, Long Papers. Association for Computational Linguistics, Vancouver, pp. 582–592 (2017)

    Google Scholar 

  18. Mihalcea, R., Tarau, P.: Textrank: Bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language, pp. 404–411 (2004)

    Google Scholar 

  19. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1), 107–117 (1998)

    Article  Google Scholar 

  20. Wan, X., Xiao, J.: Single document keyphrase extraction using neighborhood knowledge. AAAI 8, 855–860 (2008)

    Google Scholar 

  21. Bougouin, A., Boudin, F., Daille, B.: Topicrank: Graph-based topic ranking for key-phrase extraction. Int. Joint Conf. Nat. Lang. Process. (IJCNLP), 543–551 (2013)

    Google Scholar 

  22. Shinde, P., Govilkar, S.: A systematic study of text mining techniques. Int. J. Nat. Lang. Comput. 4(4), 54–62 (2015)

    Article  Google Scholar 

  23. Rousseau, F., Vazirgiannis, M.: Main core retention on graph-of-words for single document keyword extraction: Advances in Information Retrieval, 382–393 (2015)

    Google Scholar 

  24. Batagelj, V., Zaveršnik, M.: Fast algorithms for determining (generalized) core groups in social networks. Adv. Data Anal. Classif. 5(2), 129–145 (2011)

    Article  MathSciNet  Google Scholar 

  25. Wang, R., Liu, W., McDonald, C.: Corpus-independent generic key-phrase extraction using word embedding vectors. In: Proceedings of the Software Engineering Research Conference, pp. 1–8 (2014)

    Google Scholar 

  26. Florescu, C., Caragea, C.: Positionrank: An unsupervised approach to key-phrase extraction from scholarly documents. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, Long Papers, pp. 1105–1115 (2017)

    Google Scholar 

  27. Boudin, F.: Unsupervised key-phrase extraction with multipartite graphs (2018). arXiv preprint arXiv:1803.08721

  28. Škrlj, B., Repar, A., Pollak, S.: RaKUn: Rank-based keyword extraction via unsupervised learning and meta vertex aggregation. In: Proceedings of the International Conference on Statistical Language and Speech Processing, pp. 311–323. Springer, Cham (2019)

    Google Scholar 

  29. Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 216–223 (2003)

    Google Scholar 

  30. Kim, S.N., Medelyan, O., Kan, M.Y., Baldwin, T., Pingar, L.P.: Semeval-2010 task 5: Automatic key-phrase extraction from scientific articles. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 21–26 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. ENSIAS Mohammed V University in Rabat, Rabat, Morocco

    Lahbib Ajallouda & Ahmed Zellou

  2. LTIM–FSBM Hassan II University in Casablanca, Casablanca, Morocco

    Fatima Zahra Fagroud & El Habib Benlahmar

Authors
  1. Lahbib Ajallouda
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  2. Fatima Zahra Fagroud
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  3. Ahmed Zellou
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  4. El Habib Benlahmar
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Corresponding author

Correspondence to Lahbib Ajallouda .

Editor information

Editors and Affiliations

  1. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  2. UNILEHAVRE, UNIROUEN, Normandie Université, Le Havre, France

    Claude Duvallet

  3. Vrije Universiteit Brussel, Brussels, Belgium

    Abdellah Touhafi

  4. ENSA, Abdelmalek Essaâdi University, Tetuan, Morocco

    Mohammed Al Achhab

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Ajallouda, L., Fagroud, F.Z., Zellou, A., Benlahmar, E.H. (2022). Automatic Key-Phrase Extraction: Empirical Study of Graph-Based Methods. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_33

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