  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).
