The availability of metadata for scientific documents is pivotal in propelling scientific knowledge forward and for adhering to the FAIR principles (i.e. Findability, Accessibility, Interoperability, and Reusability) of research findings. However, the lack of sufficient metadata in published documents, particularly those from smaller and mid-sized publishers, hinders their accessibility. This issue is widespread in some disciplines, such as the German Social Sciences, where publications often employ diverse templates. To address this challenge, our study evaluates various feature learning and prediction methods, including natural language processing (NLP), computer vision (CV), and multimodal approaches, for extracting metadata from documents with high template variance. We aim to improve the accessibility of scientific documents and facilitate their wider use. To support our comparison of these methods, we provide comprehensive experimental results, analyzing their accuracy and efficiency in extracting metadata. Additionally, we provide valuable insights into the strengths and weaknesses of various feature learning and prediction methods, which can guide future research in this field.

本研究针对学术文献元数据缺乏的问题，尤其是来自小型和中型出版商的文献，评估多种特征学习和预测方法，包括自然语言处理和计算机视觉，以提高这些文献的可获取性。研究结果表明，不同方法在元数据提取的准确性和效率方面存在显著差异，为该领域的未来研究提供了重要的指导。

PDF学术文献元数据提取特征学习方法的比较