Abstract
In this paper, we exploit the innate document segment structure for improving the extractive summarization task. We build two text segmentation models and find the most optimal strategy to introduce their output predictions in an extractive summarization model. Experimental results on a corpus of scientific articles show that extractive summarization benefits from using a highly accurate segmentation method. In particular, most of the improvement is in documents where the most relevant information is not at the beginning thus, we conclude that segmentation helps in reducing the lead bias problem.
Abstract (translated)
在本文中,我们利用文档自身的分片结构来改进提取总结任务。我们构建了两个文本分片模型,并找到在提取总结模型中引入其输出预测的最优化策略。对科学文章集的实验结果显示,使用高精度的分片方法可以带来提取总结的益处。特别是,大部分改进出现在文档中最具相关的信息并不在开头的情况下,因此,我们得出结论,分片有助于减少领先地位偏见问题。
URL
https://arxiv.org/abs/2301.08817
