Abstract
Automatic survey generation for a specific research area can quickly give researchers an overview, and help them recognize the technical developing trend of the specific area. As far as we know, the most relevant study with automatic survey generation is the task of automatic related work generation. Almost all existing methods of automatic related work generation extract the important sentences from multiple relevant papers to assemble a related work. However, the extractive methods are far from satisfactory because of poor coherence and readability. In this paper, we propose a novel abstractive method named Hierarchical Seq2seq model based on Dual Supervision (HSDS) to solve problems above. Given multiple scientific papers in the same research area as input, the model aims to generate a corresponding survey. Furthermore, we build a large dataset to train and evaluate the HSDS model. Extensive experiments demonstrate that our proposed model performs better than the state-of-the-art baselines.
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Acknowledgment
The work is supported by NSFC (No. 61772076 and 61751201), NSFB (No. Z181100008918002), BIGKE (No. 20160754021) and CETC (No. w-2018018).
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Jiang, XJ., Mao, XL., Feng, BS., Wei, X., Bian, BB., Huang, H. (2019). HSDS: An Abstractive Model for Automatic Survey Generation. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_5
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