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

Choudhury, Muntabir Hasan; R., Jayanetti, Himarsha; Wu, Jian; Ingram, William A.; Fox, Edward A.

doi:10.1109/jcdl52503.2021.00066

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…As shown in Table II, data was missing in many fields. To mitigate the challenges of obtaining complete and consistent metadata, we developed a metadata extraction framework [1], trained on a set of human annotated ETDs. It considers both textual and visual features.…”

Section: Challenges and Lessonsmentioning

confidence: 99%

Building A Large Collection of Multi-domain Electronic Theses and Dissertations

Uddin

Banerjee

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

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In this work, we report our progress on building a collection containing over 450k Electronic Theses and Dissertations (ETDs), including full-text and metadata. Our goal is to close the gap of accessibility between long text and short text documents, and to create a new research opportunity for the scholarly community. For that, we developed an ETD Ingestion Framework (EIF) that automatically harvests metadata and PDFs of ETDs from university libraries. We faced multiple challenges and learned many lessons during the process, that led to proposed solutions to overcome/mitigate the limitations of the current data. We also described the data that we have collected. We hope our methods will be useful for building similar collections from university libraries and that the data can be used for research and education.

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Section: Challenges and Lessonsmentioning

confidence: 99%

Building A Large Collection of Multi-domain Electronic Theses and Dissertations

Uddin

Banerjee

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

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The digitization of historical astrophysical literature with highly localized figures and figure captions

2023

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Building datasets to support information extraction and structure parsing from electronic theses and dissertations

Ingram,

Wu,

Kahu

et al. 2024

Int J Digit Libr

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Despite the millions of electronic theses and dissertations (ETDs) publicly available online, digital library services for ETDs have not evolved past simple search and browse at the metadata level. We need better digital library services that allow users to discover and explore the content buried in these long documents. Recent advances in machine learning have shown promising results for decomposing documents into their constituent parts, but these models and techniques require data for training and evaluation. In this article, we present high-quality datasets to train, evaluate, and compare machine learning methods in tasks that are specifically suited to identify and extract key elements of ETD documents. We explain how we construct the datasets by manual labeling the data or by deriving labeled data through synthetic processes. We demonstrate how our datasets can be used to develop downstream applications and to evaluate, retrain, or fine-tune pre-trained machine learning models. We describe our ongoing work to compile benchmark datasets and exploit machine learning techniques to build intelligent digital libraries for ETDs.

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

Cited by 7 publications

References 9 publications

Building A Large Collection of Multi-domain Electronic Theses and Dissertations

Building A Large Collection of Multi-domain Electronic Theses and Dissertations

The digitization of historical astrophysical literature with highly localized figures and figure captions

Building datasets to support information extraction and structure parsing from electronic theses and dissertations

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