Abstract
Efficiently identifying keyphrases that represent a given document is a challenging task. In the last years, plethora of keyword detection approaches were proposed. These approaches can be based on statistical (frequency-based) properties of e.g., tokens, specialized neural language models, or a graph-based structure derived from a given document. The graph-based methods can be computationally amongst the most efficient ones, while maintaining the retrieval performance. One of the main properties, common to graph-based methods, is their immediate conversion of token space into graphs, followed by subsequent processing. In this paper, we explore a novel unsupervised approach which merges parts of a document in sequential form, prior to construction of the token graph. Further, by leveraging personalized PageRank, which considers frequencies of such sub-phrases alongside token lengths during node ranking, we demonstrate state-of-the-art retrieval capabilities while being up to two orders of magnitude faster than current state-of-the-art unsupervised detectors such as YAKE and MultiPartiteRank. The proposed method’s scalability was also demonstrated by computing keyphrases for a biomedical corpus comprised of 14 million documents in less than a minute.
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
Notes
- 1.
We considered unigrams. Inverse document frequencies were not computed as they require the whole corpus, making them not directly comparable to purely unsupervised methods.
- 2.
References
Aronson, A.R., et al.: The NLM indexing initiative. In: Proceedings of the AMIA Symposium, p. 17. American Medical Informatics Association (2000)
Augenstein, I., Das, M., Riedel, S., Vikraman, L., McCallum, A.: SemEval 2017 task 10: ScienceIE - extracting keyphrases and relations from scientific publications. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 546–555. Association for Computational Linguistics, Vancouver (2017). https://doi.org/10.18653/v1/S17-2091, https://aclanthology.org/S17-2091
Beliga, S., Meštrović, A., Martincic-Ipsic, S.: An overview of graph-based keyword extraction methods and approaches. J. Inf. Organ. Sci. 39, 1–20 (2015)
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc., Sebastopol (2009)
Boudin, F.: PKE: an open source python-based keyphrase extraction toolkit. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, Osaka, Japan, pp. 69–73, December 2016. https://aclweb.org/anthology/C16-2015
Boudin, F.: Unsupervised keyphrase extraction with multipartite graphs. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 667–672. Association for Computational Linguistics, New Orleans (2018). https://doi.org/10.18653/v1/N18-2105, https://aclanthology.org/N18-2105
Bougouin, A., Boudin, F., Daille, B.: TopicRank: graph-based topic ranking for keyphrase extraction. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 543–551. Asian Federation of Natural Language Processing, Nagoya (2013). https://aclanthology.org/I13-1062
Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., Jatowt, A.: Yake! keyword extraction from single documents using multiple local features. Inf. Sci. 509, 257–289 (2020). https://doi.org/10.1016/j.ins.2019.09.013
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(1), 1–30 (2006). https://jmlr.org/papers/v7/demsar06a.html
Ding, H., Luo, X.: AttentionRank: unsupervised keyphrase extraction using self and cross attentions. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 1919–1928. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, November 2021. https://doi.org/10.18653/v1/2021.emnlp-main.146, https://aclanthology.org/2021.emnlp-main.146
Gollapalli, S.D., Caragea, C.: Extracting keyphrases from research papers using citation networks. In: Brodley, C.E., Stone, P. (eds.) Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 27–31 July 2014, Québec City, Québec, Canada, pp. 1629–1635. AAAI Press (2014). https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8662
Grootendorst, M.: KeyBERT: minimal keyword extraction with BERT (2020). https://doi.org/10.5281/zenodo.4461265
Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1262–1273. Association for Computational Linguistics, Baltimore (2014). https://doi.org/10.3115/v1/P14-1119, https://aclanthology.org/P14-1119
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). https://aclanthology.org/W03-1028
Kim, S.N., Medelyan, O., Kan, M.Y., Baldwin, T.: SemEval-2010 task 5: automatic keyphrase extraction from scientific articles. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 21–26. Association for Computational Linguistics, Uppsala (2010). https://aclanthology.org/S10-1004
Krapivin, M., Autaeu, A., Marchese, M.: Large dataset for keyphrases extraction (2009)
Kumar, T., Mahrishi, M., Meena, G.: A comprehensive review of recent automatic speech summarization and keyword identification techniques. Artif. Intell. Ind. Appl. 111–126 (2022)
Marujo, L., Viveiros, M., da Silva Neto, J.P.: Keyphrase cloud generation of broadcast news (2013)
Medelyan, O.: Human-competitive automatic topic indexing. Ph.D. thesis, The University of Waikato (2009)
Medelyan, O., Frank, E., Witten, I.H.: Human-competitive tagging using automatic keyphrase extraction. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 1318–1327. Association for Computational Linguistics, Singapore (2009). https://aclanthology.org/D09-1137
Medelyan, O., Witten, I.H.: Domain-independent automatic keyphrase indexing with small training sets. arXiv preprint abs/10.1002 (2010). https://arxiv.org/abs/10.1002
Medelyan, O., Witten, I.H., Milne, D.: Topic indexing with Wikipedia. In: Proceedings of the AAAI WikiAI Workshop, vol. 1, pp. 19–24 (2008)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411. Association for Computational Linguistics, Barcelona (2004). https://aclanthology.org/W04-3252
Nguyen, T.D., Kan, M.-Y.: Keyphrase extraction in scientific publications. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 317–326. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77094-7_41
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report 1999-66, Stanford InfoLab (1999). https://ilpubs.stanford.edu:8090/422/, previous number = SIDL-WP-1999-0120
Papagiannopoulou, E., Tsoumakas, G.: A review of keyphrase extraction. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 10(2), e1339 (2020)
Schutz, A.T., et al.: Keyphrase extraction from single documents in the open domain exploiting linguistic and statistical methods. M. App. Sc thesis (2008)
Škrlj, B., Repar, A., Pollak, S.: RaKUn: Rank-based Keyword extraction via Unsupervised learning and meta vertex aggregation. In: Martín-Vide, C., Purver, M., Pollak, S. (eds.) SLSP 2019. LNCS (LNAI), vol. 11816, pp. 311–323. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31372-2_26
Wan, X., Xiao, J.: CollabRank: towards a collaborative approach to single-document keyphrase extraction. In: Proceedings of the 22nd International Conference on Computational Linguistics (COLING 2008), pp. 969–976. COLING 2008 Organizing Committee, Manchester, UK (2008). https://aclanthology.org/C08-1122
Wen, Z., Lu, X.H., Reddy, S.: MeDAL: medical abbreviation disambiguation dataset for natural language understanding pretraining. In: Proceedings of the 3rd Clinical Natural Language Processing Workshop, pp. 130–135. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.clinicalnlp-1.15, https://aclanthology.org/2020.clinicalnlp-1.15
Acknowledgements
The work was supported by the Slovenian Research Agency (ARRS) core research programme Knowledge Technologies (P2-0103), and projects Computer-assisted multilingual news discourse analysis with contextual embeddings (J6-2581) and Quantitative and qualitative analysis of the unregulated corporate financial reporting (J5-2554). The work was also supported by the Ministry of Culture of Republic of Slovenia through project Development of Slovene in Digital Environment (RSDO).
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 Switzerland AG
About this paper
Cite this paper
Škrlj, B., Koloski, B., Pollak, S. (2022). Retrieval-Efficiency Trade-Off of Unsupervised Keyword Extraction. In: Pascal, P., Ienco, D. (eds) Discovery Science. DS 2022. Lecture Notes in Computer Science(), vol 13601. Springer, Cham. https://doi.org/10.1007/978-3-031-18840-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-18840-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18839-8
Online ISBN: 978-3-031-18840-4
eBook Packages: Computer ScienceComputer Science (R0)