基于深度学习的泰语新闻摘要性能分析:词位置和文档长度

Sawittree Jumpathong, T. Theeramunkong, T. Supnithi, M. Okumura
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引用次数: 1

摘要

本文提出了一种基于深度学习的泰文新闻文摘的性能分析方法。分析的重点是生成摘要的原始文档中单词的位置。同时,对系统的词生成行为进行了分析。此外,我们分析了文档长度如何影响模型在原始文档单词位置方面的性能。实验结果表明,模型生成的输出摘要在TR测试数据集上比参考摘要多约1.79倍,在TPBS测试数据集上多约2.03倍。此外,模型偶尔会生成原始文档中不存在的单词,在TR测试数据集上约占摘要字数的1.68%,在TBPS测试数据集上约占摘要字数的0.88%。根据结果,发现模型生成的系统摘要中的单词与参考摘要中的单词不一致。在文档长度方面,发现模型对短文档的总结效果优于长文档。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Performance Analysis of Deep-Learning-Based Thai News Abstractive Summarization: Word Positions and Document Length
This paper presents a performance analysis of deep-learning-based Thai news abstractive summarization. The analysis focuses on the position of the words in the original document that are generated into the summary. Also, the analysis includes the behavior of word generation of the system. Moreover, we analyse how the document length affects the performance of the models regarding word positions of the original document. The result of the experiment shows that the models generated the output summary by generating most words from the beginning part more than those from the reference summary about 1.79 times on the TR testing dataset and about 2.03 times on the TPBS testing dataset. Additionally, the models occasionally generated words that do not exist in the original document about 1.68% of word number of the summary on the TR testing dataset and about 0.88% of word number of the summary on the TBPS testing dataset. According to the result, it is found that the models generated words in the system summary is not consistent with words in the reference summary. In the document length, it is found that the models can summarize a short document better than a long document.
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