Electronic theses and dissertations (ETDs) have been proposed, advocated, and generated for more than 25 years. Although ETDs are hosted by commercial or institutional digital library repositories, they are still an understudied type of scholarly big data, partially because they are usually longer than conference proceedings and journals. Segmenting ETDs will allow researchers to study sectional content. Readers can navigate to particular pages of interest, discover, and explore the content buried in these long documents. Most existing frameworks on document page classification are designed for classifying general documents and perform poorly on ETDs. In this paper, we propose ETDPC. Its backbone is a two-stream multimodal model with a cross-attention network to classify ETD pages into 13 categories. To overcome the challenge of imbalanced labeled samples, we augmented data for minority categories and employed a hierarchical classifier. ETDPC outperforms the state-of-the-art models in all categories, achieving an F1 of 0.84 -- 0.96 for 9 out of 13 categories. We also demonstrated its data efficiency. The code and data can be found on GitHub (https://github.com/lamps-lab/ETDMiner/tree/master/etd_segmentation).

翻译：电子学位论文（ETDs）的提出、推广与生成已有25余年。尽管ETD由商业或机构数字图书馆存储库托管，但其作为一类未被充分研究的学术大数据，部分原因在于其篇幅通常长于会议论文集和期刊论文。对ETD进行分段可助力研究者分析章节内容，使读者能够导航至特定感兴趣的页面，发现并探索这些长文档中埋藏的内容。现有文档页面分类框架大多针对通用文档设计，在ETD上的表现欠佳。本文提出ETDPC，其主干采用带交叉注意力网络的双流多模态模型，旨在将ETD页面划分为13个类别。为克服标注样本不平衡的挑战，我们对少数类别进行数据增强，并采用层次化分类器。ETDPC在所有类别上均超越现有最优模型，其中9个类别的F1值达0.84-0.96。我们还验证了其数据高效性。代码与数据详见GitHub (https://github.com/lamps-lab/ETDMiner/tree/master/etd_segmentation)。