自动汇总
计算机科学
自然语言处理
边界(拓扑)
认知心理学
人工智能
情报检索
心理学
数学
数学分析
作者
Jiuyi Li,Junpeng Liu,Jianjun Ma,Wei Yang,Degen Huang
出处
期刊:ACM Transactions on Asian and Low-Resource Language Information Processing
日期:2024-02-13
卷期号:23 (4): 1-18
摘要
With the successful application of deep learning, document summarization systems can produce more readable results. However, abstractive summarization still suffers from unfaithful outputs and factual errors, especially in named entities. Current approaches tend to employ external knowledge to improve model performance while neglecting the boundary information and the semantics of the entities. In this article, we propose an entity-augmented method (EAM) to encourage the model to make full use of the entity boundary information and pay more attention to the critical entities. Experimental results on three Chinese and English summarization datasets show that our method outperforms several strong baselines and achieves state-of-the-art performance on the CLTS dataset. Our method can also improve the faithfulness of the summary and generalize well to different pre-trained language models. Moreover, we propose a method to evaluate the integrity of generated entities. Besides, we adapt the data augmentation method in the FactCC model according to the difference between Chinese and English in grammar and train a new evaluation model for factual consistency evaluation in Chinese summarization.
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