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Paperant: Key Elements Generation with New Ideas

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Web and Big Data (APWeb-WAIM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12318))

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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

  1. SCIgen. https://pdos.csail.mit.edu/archive/scigen. Accessed 27 Feb 2020

  2. 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)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. Kate, R.J., Mooney, R.J.: Joint entity and relation extraction using card-pyramid parsing. In: Association for Computational Linguistics, pp. 203–212 (2010)

    Google Scholar 

  9. Yang, B., Mitchell, T.: Joint extraction of events and entities within a document context. In: Proceedings of NAACL-HLT 2016, pp. 289–299 (2016)

    Google Scholar 

  10. Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Proceedings of Text Summarization Branches Out, pp. 74–81 (2004)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Sukhbaatar, S., Weston, J., Fergus, R.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, pp. 2440–2448 (2015)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Konstas, I., Lapata, M.: A global model for concept-to-text generation. J. Artif. Intell. Res. 48, 305–346 (2013)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. 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)

    Google Scholar 

  18. 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)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. 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)

    Google Scholar 

  24. 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)

  25. 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)

    Google Scholar 

  26. 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)

    Google Scholar 

  27. 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)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. 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)

    Google Scholar 

  31. 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)

    Google Scholar 

  32. 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)

    Google Scholar 

  33. 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)

    Google Scholar 

  34. Foster, J.G., Rzhetsky, A., Evans, J.A.: Tradition and innovation in scientists research strategies. Am. Sociol. Rev. 80, 875–908 (2015)

    Google Scholar 

  35. 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)

    Google Scholar 

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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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Authors and Affiliations

  1. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China

    Xin He, Jiuyang Tang, Zhen Tan & Xiang Zhao

  2. Mininglamp Academy of Sciences, Mininglamp Technology, Beijing, China

    Zheng Yu

Authors
  1. Xin He
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  2. Jiuyang Tang
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  3. Zhen Tan
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  4. Zheng Yu
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  5. Xiang Zhao
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Corresponding author

Correspondence to Zhen Tan .

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Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Melbourne, Melbourn, NSW, Australia

    Rui Zhang

  3. Kyung Hee University, Yongin, Korea (Democratic People's Republic of)

    Young-Koo Lee

  4. Nanjing University of Information Science and Technology, Nanjing, China

    Le Sun

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60289-5

  • Online ISBN: 978-3-030-60290-1

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