From task to evaluation: an automatic text summarization review

被引:0
|
作者
Lu, Lingfeng [1 ]
Liu, Yang [1 ]
Xu, Weiqiang [1 ]
Li, Huakang [2 ]
Sun, Guozi [1 ]
机构
[1] Nanjing Univ Posts & Telecommun, Sch Comp Sci, 9 Wenyuan Rd, Nanjing 210023, Jiangsu, Peoples R China
[2] Xian Jiaotong Liverpool Univ, Sch Artificial Intelligence & Adv Comp, 111 Renai Rd, Suzhou 215123, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Automatic text summarization; Natural language generates; Real-world application; Text summarization evaluation; MODELS;
D O I
10.1007/s10462-023-10582-5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Automatic summarization is attracting increasing attention as one of the most promising research areas. This technology has been tried in various real-world applications in recent years and achieved a good response. However, the applicability of conventional evaluation metrics cannot keep up with rapidly evolving summarization task formats and ensuing indicator. After recent years of research, automatic summarization task requires not only readability and fluency, but also informativeness and consistency. Diversified application scenarios also bring new challenges both for generative language models and evaluation metrics. In this review, we analysis and specifically focus on the difference between the task format and the evaluation metrics.
引用
收藏
页码:2477 / 2507
页数:31
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