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Summarization-based Data Augmentation for Document Classification

2023-12-01 11:34:37
Yueguan Wang, Naoki Yoshinaga

Abstract

Despite the prevalence of pretrained language models in natural language understanding tasks, understanding lengthy text such as document is still challenging due to the data sparseness problem. Inspired by that humans develop their ability of understanding lengthy text from reading shorter text, we propose a simple yet effective summarization-based data augmentation, SUMMaug, for document classification. We first obtain easy-to-learn examples for the target document classification task by summarizing the input of the original training examples, while optionally merging the original labels to conform to the summarized input. We then use the generated pseudo examples to perform curriculum learning. Experimental results on two datasets confirmed the advantage of our method compared to existing baseline methods in terms of robustness and accuracy. We release our code and data at this https URL.

Abstract (translated)

尽管在自然语言处理任务中预训练语言模型的普及程度很高,但理解长篇文档,如文档,仍然具有挑战性,因为数据稀疏性问题。受到人类通过阅读较短文本来理解长篇文本的能力的启发,我们提出了一个简单而有效的基于摘要的增强数据,SUMMaug,用于文档分类。我们首先通过摘要原始训练例子来获得易于学习的目标文档分类任务的示例,同时可选择性地合并原始标签以符合摘要输入。然后使用生成的伪例子进行课程学习。在两个数据集上的实验结果证实了我们的方法与现有基线方法在鲁棒性和准确性方面的优势。我们将代码和数据发布在此处:https://www.url。

URL

https://arxiv.org/abs/2312.00513

PDF

https://arxiv.org/pdf/2312.00513.pdf


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