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Automatic text summarization for government news reports based on multiple features

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Abstract

The purpose of government news summarization is to extract the most important information from official government news reports. It is important for readers to be able to understand government news quickly in the age of information overload. Compared with other types of news, government news reports have more detailed content and possess a more normative format, resulting in greater length. To resolve the contradiction between the length of these reports and the trend of fragmented reading, this research proposes an automatic text summarization model based on multiple features. First, the features are extracted using the TF–IDF algorithm and word vector embedding method based on the bidirectional encoder representation from the transformers model. Second, we score the sentences based on the position, keywords, and similarity features. Finally, the top-ranked sentence is selected to form the summarization. To verify the effectiveness and superiority of the proposed method, Edmundson and ROUGE were adopted. First, based on the Edmundson evaluation criteria, the summarization results of various methods were scored. The score differences between ATS summarization based on the proposed method and manual summarization were minimal across consistency, grammaticality, time sequence, conciseness, and readability, with values of 0.14, 0.18, 0.12, 0.10, and 0.16, respectively. This suggests that our summarization exhibits the highest similarity to manual summarization. Second, we evaluated the summarization results using the ROUGE evaluation criteria. The results indicate that the proposed method achieved significantly higher scores compared to other models. Specifically, for character-level ROUGE-1, the P, R, and F scores reached 0.84, 0.93, and 0.88, respectively. At the word-level ROUGE-1, the P, R, and F scores were 0.81, 0.89, and 0.85, respectively, which demonstrates a noticeable improvement over other models. Furthermore, compared to manual methods, the proposed method has advantages in assisting the reader to obtain important information rapidly.

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Availability of data and materials

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This paper was supported by The National Social Science Fund of China (No.18CTQ033).

Author information

Authors and Affiliations

  1. School of Literature and Media, China Three Gorges University, Yichang, China

    Yanni Yang & Yiting Tan

  2. School of Computer and Information Technology, China Three Gorges University, Yichang, China

    Jintao Min

  3. School of Economics and Management, China Three Gorges University, Yichang, China

    Zhengwei Huang

Authors
  1. Yanni Yang
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  2. Yiting Tan
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  3. Jintao Min
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  4. Zhengwei Huang
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Contributions

YY and TY wrote the main manuscript text. MJ proposed the method and completed the experiment. TY and HZ completed the visualization of experimental results. All authors reviewed the manuscript.

Corresponding author

Correspondence to Yiting Tan.

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Yang, Y., Tan, Y., Min, J. et al. Automatic text summarization for government news reports based on multiple features. J Supercomput 80, 3212–3228 (2024). https://doi.org/10.1007/s11227-023-05599-0

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  • DOI: https://doi.org/10.1007/s11227-023-05599-0

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