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Neural abstractive summarization fusing by global generative topics

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Abstract

Various efforts have been dedicated to automatically generate coherent, condensed and informative summaries. Most concentrate on improving the capability of generating neural language models locally, but do not consider global information. In real cases, a summary is comprehensively influenced by the full content of the source text and is especially guided by its core sense. To seamlessly integrate global semantic representation into a summarization generation system, we propose to incorporate a neural generative topic matrix as an abstractive level of topic information. By mapping global semantics into a local generative language model, the abstractive summarization is capable of generating succinct and recapitulative words or phrases. Extensive experiments on DUC-2004 and Gigaword datasets convincingly validate the proposed model.

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Notes

  1. We paired the first sentence of each article with its headline to form sentence–headline summary pairs. Then, we used the PTB tokenization to pre-process the pairs.

  2. The splits of Gigaword for training can be found at https://github.com/facebook/NAMAS.

  3. Obtained from https://github.com/harvardnlp/sent-summary.

  4. It can be downloaded from http://duc.nist.gov/ with permission.

  5. The ROUGE evaluation is same as [22] which is the official ROUGE script, -m -n 2 -w 1.2.

  6. https://github.com/AEGISEDGE/GLEAM.

  7. Code is from: https://github.com/ysmiao/nvdm.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61602036, No. 61751201), and is supported by the Research Foundation of Beijing Municipal Science & Technology Commission (Grant No. Z181100008918002).

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  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Yang Gao, Yang Wang, Luyang Liu, Yidi Guo & Heyan Huang

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  1. Yang Gao
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Correspondence to Yang Gao.

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Gao, Y., Wang, Y., Liu, L. et al. Neural abstractive summarization fusing by global generative topics. Neural Comput & Applic 32, 5049–5058 (2020). https://doi.org/10.1007/s00521-018-3946-7

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