Opening scholarly documents through text analytics
Article No.: 47, Pages 1 - 2
Abstract
Vast amounts of scholarly knowledge are buried in electronic theses and dissertations (ETDs). ETDs are valuable documents that have been developed at great cost but largely remain unknown and unused. We aim for digital libraries to open up these long documents using computerized text mining and analytics. We add value to the existing systems by providing chapter-level labels and summaries. This allows readers to easily find chapters of interest. We use ETDs to fine-tune language models like BERT and SciBERT, to help better capture the specialized vocabulary present in such documents.
References
[1]
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019. SciBERT: A Pretrained Language Model for Scientific Text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 3615--3620.
[2]
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The Long-Document Transformer. arXiv:2004.05150 [cs] (Dec. 2020). http://arxiv.org/abs/2004.05150 arXiv: 2004.05150.
[3]
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. arXiv preprint arXiv:1804.05685 (2018).
[4]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186.
[5]
Palakh Mignonne Jude. June, 2020. Increasing Accessibility of Electronic Theses and Dissertations (ETDs) Through Chapter-level Classification. MS thesis, Computer Science, Virginia Tech (June, 2020). http://hdl.handle.net/10919/99294
Index Terms
- Opening scholarly documents through text analytics
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Published In
June 2022
392 pages
ISBN:9781450393454
DOI:10.1145/3529372
- General Chairs:
- Akiko Aizawa,
- Thomas Mandl,
- Zeljko Carevic,
- Program Chairs:
- Annika Hinze,
- Philipp Mayr,
- Philipp Schaer
Copyright © 2022 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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- IEEE Technical Committee on Digital Libraries (TC DL)
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 June 2022
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- Extended-abstract
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JCDL '22: The ACM/IEEE Joint Conference on Digital Libraries in 2022
June 20 - 24, 2022
Cologne, Germany
Acceptance Rates
JCDL '22 Paper Acceptance Rate 35 of 132 submissions, 27%;
Overall Acceptance Rate 415 of 1,482 submissions, 28%
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