Abstract
The extraction of meaningful keyphrases is important for a variety of applications, such as recommender systems, solutions for browsing of literature, or automatic categorization of documents. Since this task is not trivial, a great amount of different approaches have been introduced in the past, either focusing on single aspects of the process or utilizing the characteristics of a certain type of document. Especially when it comes to supporting the user in grasping the topics of a document (i.e. in the display of search results), precise keyphrases can be very helpful. However, in such situations usually only the abstract or a short excerpt is available, which most approaches do not acknowledge. Methods based on the frequency of words are not appropriate in this case, since the short texts do not contain sufficient word statistics for a frequency analysis. Secondly, many existing methods are supervised and therefore depend on domain knowledge or manually annotated data, which is in many scenarios not available. Therefore we present an unsupervised graph-based approach for extracting meaningful keyphrases from abstracts of scientific articles. We show that even though our method is not based on manually annotated data or corpora, it works surprisingly well.
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References
Baddeley, A.D., Thomson, N., Buchanan, M.: Word length and the structure of short-term memory. J. Verbal Learn. Verbal Behav. 14(6), 575–589 (1975)
Barla, M., Bieliková, M.: On deriving tagsonomies: keyword relations coming from crowd. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 309–320. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04441-0_27
Beliga, S., Meštrović, A., Martinčić-Ipšić, S.: An overview of graph-based keyword extraction methods and approaches. J. Inf. Organ. Sci. 39(1), 1–20 (2015)
Brandes, U.: A faster algorithm for betweenness centrality*. J. Math. Sociol. 25(2), 163–177 (2001)
Buyukkokten, O., Garcia-Molina, H., Paepcke, A.: Seeing the whole in parts: text summarization for web browsing on handheld devices. In: Proceedings of the 10th International Conference on World Wide Web, pp. 652–662. ACM (2001)
Freeman, L.C.: Centrality in social networks - conceptual clarification. Soc. Netw. 1, 215–239 (1978)
Goecks, J., Shavlik, J.: Learning users’ interests by unobtrusively observing their normal behavior. In: Proceedings of the 5th International Conference on Intelligent user interfaces, pp. 129–132. ACM (2000)
Grothe, L., Luca, E.W.D., Nürnberger, A.: A comparative study on language identification methods. In: Proceedings of the Sixth International Language Resources and Evaluation (LREC 2008), pp. 980–985 (2008)
Igarashi, A., Pierce, B.C., Wadler, P.: Featherweight Java: a minimal core calculus for Java and GJ. ACM Trans. Program. Lang. Syst. (1999)
Lahiri, S., Choudhury, S.R., Caragea, C.: Keyword and keyphrase extraction using centrality measures on collocation networks. arXiv:1401.6571 (2014)
Lossio-Ventura, J.A., Jonquet, C., Roche, M., Teisseire, M.: Yet another ranking function for automatic multiword term extraction. In: Przepiórkowski, A., Ogrodniczuk, M. (eds.) NLP 2014. LNCS (LNAI), vol. 8686, pp. 52–64. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10888-9_6
Popova, S., Khodyrev, I.: Ranking in keyphrase extraction problem: is it suitable to use statistics of words occurrences. Proc. Inst. Syst. Program. 26(4), 123–136 (2014)
Popova, S., Kovriguina, L., Mouromtsev, D., Khodyrev, I.: Stop-words in keyphrase extraction problem. In: 2013 14th Conference of Open Innovations Association (FRUCT), pp. 113–121. IEEE (2013)
Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. In: Berry, M.W., Kogan, J. (eds.) Text Mining, pp. 1–20. Wiley, New York (2010)
Sarıyüce, A.E., Kaya, K., Saule, E., Catalyürek, U.V.: Incremental algorithms for closeness centrality. In: IEEE International Conference on BigData (2013)
Šišović, S., Martinčić-Ipšić, S., Meštrović, A.: Toward network-based keyword extraction from multitopic web documents. In: International Conference on Information Technologies and Information Society (ITIS 2014) (2014)
Wang, R., Liu, W., McDonald, C.: Using word embeddings to enhance keyword identification for scientific publications. In: Sharaf, M.A., Cheema, M.A., Qi, J. (eds.) ADC 2015. LNCS, vol. 9093, pp. 257–268. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19548-3_21
Xie, Z.: Centrality measures in text mining: prediction of noun phrases that appear in abstracts. In: Proceedings of the ACL Student Research Workshop, pp. 103–108. Association for Computational Linguistics, Stroudsburg (2005)
Yoon, J., Kim, K.: Detecting signals of new technological opportunities using semantic patent analysis and outlier detection. Scientometrics 90(2), 445–461 (2011)
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This research is supported by BMWi grants KF3358702KM4, KF2885203KM4.
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Ludwig, P., Thiel, M., Nürnberger, A. (2017). Unsupervised Extraction of Conceptual Keyphrases from Abstracts. In: Calì, A., Gorgan, D., Ugarte, M. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2016. Lecture Notes in Computer Science(), vol 10151. Springer, Cham. https://doi.org/10.1007/978-3-319-53640-8_4
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DOI: https://doi.org/10.1007/978-3-319-53640-8_4
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