Abstract
In this work, we propose a lightweight approach for keyword extraction and ranking based on an unsupervised methodology to select the most important keywords of a single document. To understand the merits of our proposal, we compare it against RAKE, TextRank and SingleRank methods (three well-known unsupervised approaches) and the baseline TF.IDF, over four different collections to illustrate the generality of our approach. The experimental results suggest that extracting keywords from documents using our method results in a superior effectiveness when compared to similar approaches.
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Notes
- 1.
Implementation available at http://www.hlt.utdallas.edu/~saidul/code.html.
- 2.
Implementation available at https://github.com/zelandiya/RAKE-tutorial.
- 3.
Implementation available at https://pypi.python.org/pypi/yake.
References
Aquino, G., Lanzarini, L.: Keyword identification in Spanish documents using neural networks. J. Comput. Sci. Technol. 15(2), 55–60 (2015)
Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., Jatowt, A.: YAKE! collection-independent automatic keyword extractor. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018, LNCS, vol. 10772, pp. 806–810. Springer, Cham (2018)
Kim, S., Medelyan, O., Kan, M.-Y., Baldwin, T.: SemEval-2010 task 5: automatic keyphrase extraction from scientific articles. In: SemEval 2010, Sweden, pp. 21–26 (2010)
Levenshtein, V.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)
Marujo, L., Viveiros, M., Neto, J.: Keyphrase cloud generation of broadcast news. In: arXiv (2013)
Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. J. Artif. Intell. Tools 13(1), 157–169 (2004)
Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: EMNLP 2004, pp. 404–411 (2004)
Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic Keyword Extraction from Individual Documents. Text Mining: Theory and Applications. Wiley, Chichester (2010)
Schutz, A.T.: Keyphrase extraction from single documents in the open domain exploiting linguistic and statistical methods. Master thesis, National University of Ireland (2008)
Wan, X., Xiao, J.: Single document keyphrase extraction using neighborhood knowledge. In: AAAI 2008, 13–17 July, pp. 855–860 (2008)
Witten, I., Paynter, G., Frank, E., Gutwin, C., Nevill-Manning, C.: KEA: practical automatic keyphrase extraction. In: Proceedings of the JCDL 2004, 7–11 June, pp. 254–255 (1999)
Acknowledgements
This work is partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013 and of project UID/MAT/00212/2013. It was also financed by MIC SCOPE (171507010) and by Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” which is financed by the NORTE 2020, under the PORTUGAL 2020, and through the ERDF.
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Campos, R., Mangaravite, V., Pasquali, A., Jorge, A.M., Nunes, C., Jatowt, A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_63
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