Paper Info
Title
The Newspaper Navigator Dataset: Extracting Headlines and Visual Content from 16 Million Historic Newspaper Pages in Chronicling America
Abstract
Chronicling America is a product of the National Digital Newspaper Program, a partnership between the Library of Congress and the National Endowment for the Humanities to digitize historic American newspapers. Over 16 million pages have been digitized to date, complete with high-resolution images and machine-readable METS/ALTO OCR. Of considerable interest to Chronicling America users is a semantified corpus, complete with extracted visual content and headlines. To accomplish this, we introduce a visual content recognition model trained on bounding box annotations collected as part of the Library of Congress's Beyond Words crowdsourcing initiative and augmented with additional annotations including those of headlines and advertisements. We describe our pipeline that utilizes this deep learning model to extract 7 classes of visual content: headlines, photographs, illustrations, maps, comics, editorial cartoons, and advertisements, complete with textual content such as captions derived from the METS/ALTO OCR, as well as image embeddings. We report the results of running the pipeline on 16.3 million pages from the Chronicling America corpus and describe the resulting Newspaper Navigator dataset, the largest dataset of extracted visual content from historic newspapers ever produced. The Newspaper Navigator dataset, finetuned visual content recognition model, and all source code are placed in the public domain for unrestricted re-use.
Year
DOI
Venue
2020
10.1145/3340531.3412767
arxiv
DocType
ISBN
Citations 
Conference
978-1-4503-6859-9
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Germain Lee Benjamin Charles100.34
Jaime Mears200.34
Eileen Jakeway300.34
Meghan Ferriter400.34
Chris Adams500.34
Nathan Yarasavage600.68
Deborah Thomas700.34
Kate Zwaard800.34
Daniel S. Weld9102981127.49
Benjamin Charles Germain Lee1000.34