Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)

<Previous Article In Volume
Next Article In Volume>

Building Method of a BERT-Based Model for Key Information Extraction from Chemical Engineering Literature

Authors
Zhenhua Liu1, Shoulong Ma1, *
1School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230000, Anhui, China
*Corresponding author. Email: 398007321@qq.com
Corresponding Author
Shoulong Ma
Available Online 22 September 2023.
DOI
10.2991/978-94-6463-242-2_51How to use a DOI?
Keywords
Literature reading; information extraction; BERT model; text summarization
Abstract

For researchers, it is essential to access various literature in order to stay up-to-date with the latest advancements and trends in scientific research. Chemical engineering literature, characterized by its diverse range, lengthy articles, complex experimental conditions, and numerous references to chemical compounds, poses a challenge in terms of manually extracting research content from a massive volume of literature. Relying solely on human effort to extract information from chemical engineering literature would be extremely time-consuming and resource-intensive. To enhance the speed at which chemical engineering researchers acquire knowledge and reduce the time spent on reading literature, this paper proposes a text summarization model based on BERT (Bidirectional Encoder Representations from Transformers). The model aims to generate concise summaries corresponding to the key information found in chemical engineering literature. Based on the textual content generated by the model, it has achieved satisfactory results in extracting key information from chemical engineering literature.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
22 September 2023
ISBN
10.2991/978-94-6463-242-2_51
ISSN
2589-4900
DOI
10.2991/978-94-6463-242-2_51How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zhenhua Liu
AU  - Shoulong Ma
PY  - 2023
DA  - 2023/09/22
TI  - Building Method of a BERT-Based Model for Key Information Extraction from Chemical Engineering Literature
BT  - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
PB  - Atlantis Press
SP  - 410
EP  - 417
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-242-2_51
DO  - 10.2991/978-94-6463-242-2_51
ID  - Liu2023
ER  -