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20212025
most citedAutomating Chapter-Level Classification for Electronic Theses and Dissertations

2 citations · 3 across the 4 of their papers we have counts for

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cs.DL2025

Toward Robust URL Extraction for Open Science: A Study of arXiv File Formats and Temporal Trends

Rochana R. Obadage, Lamia Salsabil, Sawood Alam +4

In this work, we study how URL extraction results depend on input format. We compiled a pilot dataset by extracting URLs from 10 arXiv papers and used the same heuristic method to…

cs.DL20242 cited

Automating Chapter-Level Classification for Electronic Theses and Dissertations

Bipasha Banerjee, William A. Ingram, Edward A. Fox

Traditional archival practices for describing electronic theses and dissertations (ETDs) rely on broad, high-level metadata schemes that fail to capture the depth, complexity, and…

cs.DL2024

Making History Readable

Bipasha Banerjee, Jennifer Goyne, William A. Ingram

The Virginia Tech University Libraries (VTUL) Digital Library Platform (DLP) hosts digital collections that offer our users access to a wide variety of documents of historical and…

cs.DL2024

Agentic AI for Improving Precision in Identifying Contributions to Sustainable Development Goals

William A. Ingram, Bipasha Banerjee, Edward A. Fox

As research institutions increasingly commit to supporting the United Nations' Sustainable Development Goals (SDGs), there is a pressing need to accurately assess their research ou…

cs.DL2021

Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations

Muntabir Hasan Choudhury, Himarsha R. Jayanetti, Jian Wu +2

Electronic Theses and Dissertations (ETDs) contain domain knowledge that can be used for many digital library tasks, such as analyzing citation networks and predicting research tre…