基于改进Transformer的电信重投报告自动生成方法研究

梁伟明; 肖军; 马晓亮; 辛盛; 徐荣彬

doi:10.11959/j.issn.1000-0801.2025110

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基于改进Transformer的电信重投报告自动生成方法研究

工程与应用 | 更新时间：2025-06-28

- 基于改进Transformer的电信重投报告自动生成方法研究
- Research on the automatic generation method of telecom re-complaints report based on improved Transformer model
- 电信科学 2025年41卷第6期页码：197-207
- 作者机构：
  
  1.中国电信股份有限公司广州分公司，广东广州 510620
  2.中数通信息有限公司，广东广州 510630
  3.马晓亮劳模与工匠人才创新工作室，广东广州 510620
- 作者简介：
  
  [ "梁伟明（1971- ），男，中国电信股份有限公司广州分公司客户服务部总经理、" ]
  [ "肖军（1974- ），男，中数通信息有限公司业务总监、" ]
  [ "马晓亮（1973- ），男，博士，中国电信股份有限公司广州分公司副总经理、正高级工程师，马晓亮劳模和工匠人才创新工作室领衔人，主要研究方向为人工智能、自然语言处理和数据安全保护等。" ]
  [ "辛盛（1975- ），男，中数通信息有限公司技术总监、" ]
  [ "徐荣彬（1988- ），男，中数通信息有限公司项目部经理、" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025110
  中图分类号： TP399
- 收稿日期：2024-12-11，
  
  修回日期：2025-04-07，
  
  纸质出版日期：2025-06-20
- 稿件说明：
移动端阅览
梁伟明,肖军,马晓亮等.基于改进Transformer的电信重投报告自动生成方法研究[J].电信科学,2025,41(06):197-207.

LIANG Weiming,XIAO Jun,MA Xiaoliang,et al.Research on the automatic generation method of telecom re-complaints report based on improved Transformer model[J].Telecommunications Science,2025,41(06):197-207.
梁伟明,肖军,马晓亮等.基于改进Transformer的电信重投报告自动生成方法研究[J].电信科学,2025,41(06):197-207. DOI： 10.11959/j.issn.1000-0801.2025110.

LIANG Weiming,XIAO Jun,MA Xiaoliang,et al.Research on the automatic generation method of telecom re-complaints report based on improved Transformer model[J].Telecommunications Science,2025,41(06):197-207. DOI： 10.11959/j.issn.1000-0801.2025110.

摘要

在电信行业中，客户对未解决或处理不满意的投诉进行重复投诉的现象较为常见。手动生成重投报告不仅耗时且主观性较强，难以满足企业对高效性和一致性的要求。针对这一问题，提出了一种基于改进Transformer模型的自动化报告生成方法。该方法通过引入情绪嵌入，有效捕捉客户在对话中的情绪变化，改善了生成报告对客户态度和诉求的理解能力。同时，结合定制化位置编码，提升了模型对投诉时序信息的感知能力，从而增强了生成内容的时间逻辑性和细节完整性。实验结果表明，改进后的模型在BLEU（bilingual evaluation understudy）和ROUGE（recall-oriented understudy for gisting evaluation）指标上分别达到0.352和0.482，显著优于原始Transformer和其他对比模型。此外，与人工对比，工作效率提高了89%。生成的报告内容不仅更加准确贴合实际需求，还在语义细节与时序一致性上表现优异。

Abstract

In the telecommunications industry

repeated customer complaints about unresolved or unsatisfactory issues are a common challenge. Manually generating re-investment reports is not only time-consuming and prone to subjectivity but also fails to meet enterprise demands for efficiency and consistency. To address this issue

an automatic report generation method based on an improved Transformer model was proposed. This method introduced emotion embedding

enabling the model to effectively capture dynamic emotional changes in customer interactions and better understand customer attitudes and demands during the dialogue. Additionally

the incorporation of customized position encoding enhanced the model’s ability to perceive complaint time series information

significantly improving the time logic and detailed completeness of the generated content. Experimental results demonstrate that the proposed model achieves BLEU (bilingual evaluation understudy) and ROUGE (recall-oriented understudy for gisting evaluation) scores of 0.352 and 0.482

respectively

outperforming the original Transformer and other baseline models. Moreover

compared to manual efforts

the proposed model improves work efficiency by 89%. The generated reports not only align more accurately with real-world requirements but also exhibit superior performance in semantic detail and time sequence consistency.

关键词

Keywords

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