@inproceedings{lo-etal-2023-papermage,
title = "{P}aper{M}age: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents",
author = "Lo, Kyle and
Shen, Zejiang and
Newman, Benjamin and
Chang, Joseph and
Authur, Russell and
Bransom, Erin and
Candra, Stefan and
Chandrasekhar, Yoganand and
Huff, Regan and
Kuehl, Bailey and
Singh, Amanpreet and
Wilhelm, Chris and
Zamarron, Angele and
Hearst, Marti A. and
Weld, Daniel and
Downey, Doug and
Soldaini, Luca",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.45",
doi = "10.18653/v1/2023.emnlp-demo.45",
pages = "495--507",
abstract = "Despite growing interest in applying natural language processing (NLP) and computer vision (CV) models to the scholarly domain, scientific documents remain challenging to work with. They{'}re often in difficult-to-use PDF formats, and the ecosystem of models to process them is fragmented and incomplete. We introduce PaperMage, an open-source Python toolkit for analyzing and processing visually-rich, structured scientific documents. PaperMage offers clean and intuitive abstractions for seamlessly representing and manipulating both textual and visual document elements. PaperMage achieves this by integrating disparate state-of-the-art NLP and CV models into a unified framework, and provides turn-key recipes for common scientific document processing use-cases. PaperMage has powered multiple research prototypes of AI applications over scientific documents, along with Semantic Scholar{'}s large-scale production system for processing millions of PDFs. GitHub: https://github.com/allenai/papermage",
}
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<abstract>Despite growing interest in applying natural language processing (NLP) and computer vision (CV) models to the scholarly domain, scientific documents remain challenging to work with. They’re often in difficult-to-use PDF formats, and the ecosystem of models to process them is fragmented and incomplete. We introduce PaperMage, an open-source Python toolkit for analyzing and processing visually-rich, structured scientific documents. PaperMage offers clean and intuitive abstractions for seamlessly representing and manipulating both textual and visual document elements. PaperMage achieves this by integrating disparate state-of-the-art NLP and CV models into a unified framework, and provides turn-key recipes for common scientific document processing use-cases. PaperMage has powered multiple research prototypes of AI applications over scientific documents, along with Semantic Scholar’s large-scale production system for processing millions of PDFs. GitHub: https://github.com/allenai/papermage</abstract>
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%0 Conference Proceedings
%T PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents
%A Lo, Kyle
%A Shen, Zejiang
%A Newman, Benjamin
%A Chang, Joseph
%A Authur, Russell
%A Bransom, Erin
%A Candra, Stefan
%A Chandrasekhar, Yoganand
%A Huff, Regan
%A Kuehl, Bailey
%A Singh, Amanpreet
%A Wilhelm, Chris
%A Zamarron, Angele
%A Hearst, Marti A.
%A Weld, Daniel
%A Downey, Doug
%A Soldaini, Luca
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lo-etal-2023-papermage
%X Despite growing interest in applying natural language processing (NLP) and computer vision (CV) models to the scholarly domain, scientific documents remain challenging to work with. They’re often in difficult-to-use PDF formats, and the ecosystem of models to process them is fragmented and incomplete. We introduce PaperMage, an open-source Python toolkit for analyzing and processing visually-rich, structured scientific documents. PaperMage offers clean and intuitive abstractions for seamlessly representing and manipulating both textual and visual document elements. PaperMage achieves this by integrating disparate state-of-the-art NLP and CV models into a unified framework, and provides turn-key recipes for common scientific document processing use-cases. PaperMage has powered multiple research prototypes of AI applications over scientific documents, along with Semantic Scholar’s large-scale production system for processing millions of PDFs. GitHub: https://github.com/allenai/papermage
%R 10.18653/v1/2023.emnlp-demo.45
%U https://aclanthology.org/2023.emnlp-demo.45
%U https://doi.org/10.18653/v1/2023.emnlp-demo.45
%P 495-507
Markdown (Informal)
[PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents](https://aclanthology.org/2023.emnlp-demo.45) (Lo et al., EMNLP 2023)
ACL
- Kyle Lo, Zejiang Shen, Benjamin Newman, Joseph Chang, Russell Authur, Erin Bransom, Stefan Candra, Yoganand Chandrasekhar, Regan Huff, Bailey Kuehl, Amanpreet Singh, Chris Wilhelm, Angele Zamarron, Marti A. Hearst, Daniel Weld, Doug Downey, and Luca Soldaini. 2023. PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 495–507, Singapore. Association for Computational Linguistics.