ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations
This work addresses the need for better segmentation and navigation of ETDs, a specific type of scholarly big data, but it is incremental as it adapts existing multimodal methods to a niche domain.
The paper tackled the problem of classifying pages in electronic theses and dissertations (ETDs), which are understudied due to their length, by proposing ETDPC, a multimodal framework that achieved F1 scores of 0.84 to 0.96 for 9 out of 13 categories, outperforming state-of-the-art models.
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).