论文标题
基于序列的科学文章的提取性摘要
Sequence-Based Extractive Summarisation for Scientific Articles
论文作者
论文摘要
本文介绍了有关科学文章的监督提取文本摘要的研究结果。我们表明，仅基于文档中文本的简单顺序标记模型，可以针对简单的分类模型获得很高的结果。尽管这些句子很小，但可以通过其他句子级特征来实现改进。通过进一步的分析，我们根据文档所来自的学术纪律来显示顺序模型的潜力。
This paper presents the results of research on supervised extractive text summarisation for scientific articles. We show that a simple sequential tagging model based only on the text within a document achieves high results against a simple classification model. Improvements can be achieved through additional sentence-level features, though these were minimal. Through further analysis, we show the potential of the sequential model relying on the structure of the document depending on the academic discipline which the document is from.
