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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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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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Correspondence to Zhen Tan .

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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