Abstract
Recent work on data-to-text generation has made progress under the neural encoder-decoder architectures. However, the data input size is often enormous, while not all data records are important for text generation and inappropriate input may bring noise into the final output. To solve this problem, we propose a two-step approach which first selects and orders the important data records and then generates text from the noise-reduced data. Here we propose a learning to rank model to rank the importance of each record which is supervised by a relation extractor. With the noise-reduced data as input, we implement a text generator which sequentially models the input data records and emits a summary. Experiments on the ROTOWIRE dataset verifies the effectiveness of our proposed method in both performance and efficiency.
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References
Bosselut, A., Celikyilmaz, A., He, X., Gao, J., Huang, P.S., Choi, Y.: Discourse-aware neural rewards for coherent text generation. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), vol. 1, pp. 173–184 (2018)
Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning, pp. 129–136. ACM (2007)
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4(Nov), 933–969 (2003)
Holmes-Higgin, P.: Text generation-using discourse strategies and focus constraints to generate natural language text by Kathleen R. Mckeown, Cambridge University Press, 1992, pp 246,£ 13.95, ISBN 0-521-43802-0. Knowl. Eng. Rev. 9(4), 421–422 (1994)
Kukich, K.: Design of a knowledge-based report generator. In: 21st Annual Meeting of the Association for Computational Linguistics (1983). http://aclweb.org/anthology/P83-1022
Lebret, R., Grangier, D., Auli, M.: Neural text generation from structured data with application to the biography domain. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, 1–4 November 2016, pp. 1203–1213 (2016)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003–1011. Association for Computational Linguistics (2009). http://aclweb.org/anthology/P09-1113
Novikova, J., Dušek, O., Rieser, V.: The E2E dataset: new challenges for end-to-end generation. In: Proceedings of the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Saarbrücken, Germany (2017). https://arxiv.org/abs/1706.09254. arXiv:1706.09254
Perez-Beltrachini, L., Lapata, M.: Bootstrapping generators from noisy data. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), vol. 1, pp. 1516–1527 (2018)
Puduppully, R., Dong, L., Lapata, M.: Data-to-text generation with content selection and planning. arXiv preprint arXiv:1809.00582 (2018)
Reiter, E., Dale, R.: Building applied natural language generation systems. Nat. Lang. Eng. 3(1), 57–87 (1997)
dos Santos, C., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 626–634 (2015)
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 (Volume 1: Long Papers), vol. 1, pp. 1073–1083 (2017)
Sha, L., Mou, L., Liu, T., Poupart, P., Li, S., Chang, B., Sui, Z.: Order-planning neural text generation from structured data. arXiv preprint arXiv:1709.00155 (2017)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Wiseman, S., Shieber, S.M., Rush, A.M.: Challenges in data-to-document generation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9–11 September 2017, pp. 2253–2263 (2017)
Zhang, Z.: Weakly-supervised relation classification for information extraction. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 581–588. ACM (2004)
Acknowledgement
We thank the anonymous reviewers for their helpful comments on this paper. This work was partially supported by National Key Research and Development Project (2019YFB1704002) and National Natural Science Foundation of China (61876009 and 61572049). The corresponding author of this paper is Sujian Li.
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Liu, Q., Li, T., Guan, W., Li, S. (2020). Refining Data for Text Generation. In: Sun, M., Li, S., Zhang, Y., Liu, Y., He, S., Rao, G. (eds) Chinese Computational Linguistics. CCL 2020. Lecture Notes in Computer Science(), vol 12522. Springer, Cham. https://doi.org/10.1007/978-3-030-63031-7_7
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