Abstract
Encoder–decoder models have achieved high performance in their application to keyphrase generation. However, keyphrases for a source text generated by these models are similar to each other because each keyphrase is independently generated. To improve the diversity, we propose a model that iteratively generates each keyphrase while considering the formerly generated keyphrase. The experimentally obtained results indicate that our model generates more diverse keyphrases with a performance that is superior or comparable to conventional models.
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Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bahuleyan, H., Mou, L., Vechtomova, O., Poupart, P.: Variational attention for sequence-to-sequence models. arXiv preprint arXiv:1712.08207 (2017)
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, pp. 69–73 (2016)
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, vol. 2, pp. 667–672 (2018)
Cibils, A., Musat, C., Hossman, A., Baeriswyl, M.: Diverse beam search for increased novelty in abstractive summarization. arXiv preprint arXiv:1802.01457 (2018)
Florescu, C., Caragea, C.: Positionrank: an unsupervised approach to keyphrase extraction from scholarly documents. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 1105–1115 (2017)
Gimpel, K., Batra, D., Dyer, C., Shakhnarovich, G.: A systematic exploration of diversity in machine translation. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1100–1111 (2013)
Ian, H.W., Gordon, W.P., Frank, E., Gutwin, C., Craig, G.N.M.: Kea: practical automatic keyphrase extraction. In: Proceedings of the Fourth ACM Conference on Digital Libraries, pp. 254–255 (1999)
Li, J., Galley, M., Brockett, C., Gao, J., Dolan, B.: A diversity-promoting objective function for neural conversation models. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 110–119 (2016)
Li, J., Jurafsky, D.: Mutual information and diverse decoding improve neural machine translation. arXiv preprint arXiv:1601.00372 (2016)
Mahata, D., Kuriakose, J., Shah, R.R., Zimmermann, R.: Key2vec: automatic ranked keyphrase extraction from scientific articles using phrase embeddings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 2, pp. 634–639 (2018)
Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., Chi, Y.: Deep keyphrase generation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 582–592 (2017)
Nema, P., Khapra, M.M., Laha, A., Ravindran, B.: Diversity driven attention model for query-based abstractive summarization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 1063–1072 (2017)
Nguyen, T.D., Luong, M.T.: Wingnus: keyphrase extraction utilizing document logical structure. In: Proceedings of the Fifth International Workshop on Semantic Evaluation, pp. 166–169 (2010)
Prasad, A., Kan, M.Y.: Wing-nus at semeval-2017 task 10: Keyphrase extraction and classification as joint sequence labeling. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 973–977 (2017)
See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 1073–1083 (2017)
Shen, X., Su, H., Niu, S., Demberg, V.: Improving variational encoder-decoders in dialogue generation. arXiv preprint arXiv:1802.02032 (2018)
Vijayakumar, A.K., et al.: Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424 (2016)
Yao, T., Pan, Y., Li, Y., Mei, T.: Incorporating copying mechanism in image captioning for learning novel objects. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5263–5271 (2017)
Zhang, Y., Li, J., Song, Y., Zhang, C.: Encoding conversation context for neural keyphrase extraction from microblog posts. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1676–1686 (2018)
Zhao, T., Zhao, R., Eskenazi, M.: Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 654–664 (2017)
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Misawa, S., Miura, Y., Taniguchi, M., Ohkuma, T. (2019). Multiple Keyphrase Generation Model with Diversity. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_64
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DOI: https://doi.org/10.1007/978-3-030-15712-8_64
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