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FCSF-TABS: two-stage abstractive summarization with fact-aware reinforced content selection and fusion

  • S. I. : Effective and Efficient Deep Learning
  • Published:
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Abstract

In the era of big data, machine summarization models provide a new and efficient way for the rapid processing of massive text data. Generally, whether the fact descriptions in generated summaries are consistent with input text that is a critical metric in real-world tasks. However, most existing approaches based on standard likelihood training ignore this problem and only focus on improving the ROUGE scores. In this paper, we propose a two-stage Transformer-based abstractive summarization model to improve the factual correctness, denoted as FCSF-TABS. In the first stage, we use fine-tuned BERT classifier to perform content selection to select summary-worthy single sentences or adjacent sentence pairs in the input document. In the second stage, we feed the selected sentences into the Transformer-based summarization model to generate summary sentences. Furthermore, during the training, we also introduce the idea of reinforcement learning to jointly optimize a mixed-objective loss function. Specially, to train our model, we elaborately constructed two training sets by comprehensively considering informativeness and factual consistency. We conduct a lot of experiments on the CNN/DailyMail and XSum datasets. Experimental results show that our FCSF-TABS model not only improves the ROUGE scores, but also contains fewer factual errors in the generated summaries compared to some popular summarization models.

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Acknowledgements

Our work was supported by Guangxi Science and Technology Foundation (2019GXNSFGA245004, 2018GXNSFAA138116), the National Natural Science Foundation of China (61862011).

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Authors and Affiliations

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China

    Mengli Zhang, Gang Zhou, Wanting Yu, Ningbo Huang & Ze Yu

  2. Guilin University of Electronic Technology, Guilin, China

    Wenfen Liu

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  1. Mengli Zhang
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Correspondence to Mengli Zhang.

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Zhang, M., Zhou, G., Yu, W. et al. FCSF-TABS: two-stage abstractive summarization with fact-aware reinforced content selection and fusion. Neural Comput & Applic 34, 10547–10560 (2022). https://doi.org/10.1007/s00521-021-06880-0

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