Abstract
Long text generation, leveraging one sentence to generate a meaningful paper, is an effective method to reduce repetitive works. Conventional text generation models utilize rule-based and plan-based methods to produce paper, such as SCIgen, which is hard to suit the complex sematic scene. Recently, several neural network-based models, such as Point Network and PaperRobot, were proposed to tackle the problem, and achieve state-of-the-art performance. However, most of them only try to generate part of the paper, and ignore the sematic information of each entity in input sentence. In this paper, we present a novel method named Paperant, which leverage not only multi-sentence features to describe latent features of each entity, but also hybrid structure to generate different parts of the paper. In experiment, Paperant was superior to other methods on each indicator.
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References
SCIgen. https://pdos.csail.mit.edu/archive/scigen. Accessed 27 Feb 2020
Wei, C.H., Kao, H.Y., Lu, Z.: PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res. 41, 518–522 (2013)
Bordes, A., Usunier, N., Duran, A.G., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Luo, G., Huang, X., Lin, C.Y., Nie, Z.: Joint named entity recognition and disambiguation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 879–888 (2015)
Passos, A., Kumar, V., McCallum, A.: Lexicon infused phrase embeddings for named entity resolution. In: Proceedings of the Eighteenth Conference on Computational Language Learning, pp. 78–86 (2014)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pp. 1003–1011 (2009)
Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 2124–2133 (2016)
Kate, R.J., Mooney, R.J.: Joint entity and relation extraction using card-pyramid parsing. In: Association for Computational Linguistics, pp. 203–212 (2010)
Yang, B., Mitchell, T.: Joint extraction of events and entities within a document context. In: Proceedings of NAACL-HLT 2016, pp. 289–299 (2016)
Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Proceedings of Text Summarization Branches Out, pp. 74–81 (2004)
Wang, Z., Zhang, J., Feng, J.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)
Sukhbaatar, S., Weston, J., Fergus, R.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, pp. 2440–2448 (2015)
Madotto, A., Wu, C.S., Fung, P.: Mem2Seq: effectively incorporating knowledge bases into end-to-end task-oriented dialog systems. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1468–1478 (2018)
Konstas, I., Lapata, M.: A global model for concept-to-text generation. J. Artif. Intell. Res. 48, 305–346 (2013)
Xu, K., Wu, L., Wang, Z., Feng, Y., Sheinin, V.: SQL-to-text generation with graph-to-sequence model. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 931–936 (2018)
Lu, D., Whitehead, S., Huang, L., Ji, H., Chang, S.F.: Entity-aware image caption generation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4013–4023 (2018)
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)
Qiang, Y., Fu, Y., Guo, Y., Zhou, Z., Sigal, L.: Learning to generate posters of scientific papers. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 155–169 (2016)
Wang, Q., et al.: PaperRobot: incremental draft generation of scientific ideas. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1980–1991 (2019)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)
Krishnamoorthy, N., Malkarnenkar, G., Mooney, R.J., Saenko, K., Guadarrama, S.: Generating natural-language video descriptions using text-mined knowledge. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence, pp. 10–19 (2013)
Vadapalli, R., Syed, B., Prabhu, N., Srinivasan, B.V., Varma, V.: When science journalism meets artificial intelligence: an interactive demonstration. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 163–168 (2018)
Denkowski, M., Lavie, A.: Meteor universal: language specific translation evaluation for any target language. In: Proceedings of the 9th Workshop on Statistical Machine Translation, pp. 376–380 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of the 5th International Conference on Learning Representations. arXiv:1409.0473 (2015)
Wang, Q., et al.: Paper abstract writing through editing mechanism. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 260–265 (2018b)
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 687–696 (2015a)
Wang, Z., Li, J.Z.: Text-enhanced representation learning for knowledge graph. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 1293–1299 (2016)
Xu, J., Chen, K., Qiu, X., Huang, X.: Knowledge graph representation with jointly structural and textual encoding. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1318–1324 (2017)
Duma, D., Klein, E.: Generating natural language from linked data: unsupervised template extraction. In: Proceedings of the 10th International Conference on Computational Semantics, pp. 83–94 (2013)
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, pp. 1203–1213 (2016)
Chisholm, A., Radford, W., Hachey, B.: Learning to generate one-sentence biographies from Wikidata. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pp. 633–642 (2017)
Wu, Q., Shen, C., Wang, P., Dick, A., Hengel, A.: Image captioning and visual question answering based on attributes and external knowledge. In: Proceedings of the 2018 IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1367–1381 (2018)
Whitehead, S., Ji, H., Bansal, M., Chang, S.F., Voss, C.: Incorporating background knowledge into video description generation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3992–4001 (2018)
Foster, J.G., Rzhetsky, A., Evans, J.A.: Tradition and innovation in scientists research strategies. Am. Sociol. Rev. 80, 875–908 (2015)
Papineni, K., Roukos, S., Ward, T., Zhu, W.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
Acknowledgements
This work was partially supported by NSFC under grants Nos. 61872446, 61701454, 61902417, and 71971212, and NSF of Hunan province under grant No. 2019JJ20024.
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He, X., Tang, J., Tan, Z., Yu, Z., Zhao, X. (2020). Paperant: Key Elements Generation with New Ideas. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_23
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DOI: https://doi.org/10.1007/978-3-030-60290-1_23
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