Development of an Extractive Title Generation System Using Titles of Papers of Top Conferences for Intermediate English Students
Development of an Extractive Title Generation System Using Titles of Papers of Top Conferences for Intermediate English Students
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DOI：
10.1109/iiai-aai53430.2021.00010
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Kento Kaku;M. Kikuchi;Tadachika Ozono;T. Shintani
中科院分区：
文献类型：
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作者：
Kento Kaku;M. Kikuchi;Tadachika Ozono;T. Shintani
对于中级英语作者（尤其是学生）来说，用英语制定良好的学术论文标题具有挑战性。这是因为这些作者不知道通常使用的标题类型。我们的目标是实现一个支持系统，为中级英语和初级作者制定更有效的英语标题。本研究开发了一种提取标题生成系统，该系统根据从摘要中提取的关键字来制定标题。此外，我们实现了一个标题评估模型，可以评估论文标题的适当性。我们使用 BERT 用顶级会议论文的标题来训练模型。本文描述了训练数据、实现和实验结果。结果表明，我们的评估模型比中级英语和初级学生更能有效地识别顶级会议标题。
The formulation of good academic paper titles in English is challenging for intermediate English authors (particularly students). This is because such authors are not aware of the type of titles that are generally in use. We aim to realize a support system for formulating more effective English titles for intermediate English and beginner authors. This study develops an extractive title generation system that formulates titles from keywords extracted from an abstract. Moreover, we realize a title evaluation model that can evaluate the appropriateness of paper titles. We train the model with titles of top-conference papers by using BERT. This paper describes the training data, implementation, and experimental results. The results show that our evaluation model can identify top-conference titles more effectively than intermediate English and beginner students.