Abstract
Automatic Keyphrase Extraction describes the process of extracting keywords or keyphrases from the body of a document. To our knowledge until now all algorithms rely on a set of manually crafted statistical features to model word importance. In this paper we propose an end-to-end neural keyphrase extraction algorithm using a siamese LSTM network, eliminating the need for manual feature engineering. We train and evaluate our model on the Inspec [6] dataset for keyphrase extraction and achieve comparable results to state-of-the-art algorithms.
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Notes
- 1.
Hasan and Ng give a tabular overview over available keyword extraction datasets [4].
References
Baziotis, C., Pelekis, N., Doulkeridis, C.: Datastories at semeval-2017 task 6: Siamese lstm with attention for humorous text comparison. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 381–386. Association for Computational Linguistics, Vancouver (2017)
Fox, C.: A stop list for general text. SIGIR Forum 24(1–2), 19–21 (1989). https://doi.org/10.1145/378881.378888
Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1019–1027 (2016)
Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
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. Association for Computational Linguistics (2003)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
Liu, Z., Li, P., Zheng, Y., Sun, M.: Clustering to find exemplar terms for keyphrase extraction. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 257–266. Association for Computational Linguistics (2009)
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, vol. 3, pp. 1318–1327. Association for Computational Linguistics (2009)
Mihalcea, R., Tarau, P.: Textrank: bringing order into text. In: Proceedings 9th Conference on Empirical Methods in Natural Language Processing (EMNLP 2004) (2004)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. https://nlp.stanford.edu/projects/glove/
Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. Text Mining: Applications and Theory, pp. 1–20 (2010)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Turney, P.D.: Learning algorithms for keyphrase extraction. CoRR cs.LG/0212020 (2002). http://arxiv.org/abs/cs.LG/0212020
Wang, J., Liu, W., McDonald, C.: Corpus-independent generic keyphrase extraction using word embedding vectors (2015)
Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: practical automatic keyphrase extraction. In: Proceedings of the Fourth ACM Conference on Digital Libraries, pp. 254–255. ACM (1999)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A.J., Hovy, E.H.: Hierarchical attention networks for document classification (2016)
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Villmow, J., Wrzalik, M., Krechel, D. (2018). Automatic Keyphrase Extraction Using Recurrent Neural Networks. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_16
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