Assessing contributions of scientific articles using transformer-based deep learning
M.Sc. Student: Qi He | Advisor: Jimmy Chih-Hsien Peng | Project Duration: 2023-2024
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The number of published scientific articles is increasing at an exponential rate per year. Their claimed contributions may overlap to a certain extent, which is difficult to identify until the reader has reviewed all the works. To optimise the effort in conducting a literature review for research, e.g., in the field of power engineering, there is a need to develop a tool that can quantitatively assess the contributions of scientific articles. Leveraging the advancements in transformer-based learning algorithms, this project seeks to develop a tool that will evaluate the quality of a given article.
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Quickly Start
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01
Preparation
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OpenAI api key​ by following this link: How to get an Openai api keys?
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Nougat api path can be installed using this link: install Nougat ​
(Recommend using pytorch GPU environment)
02
Visit the website
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Click here to visit !
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03
Settings
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Fill in your Openai api key and nougat path.
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Click Save button.
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Start to use
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Upload your PDF paper files.
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Wait for the analysis to complete.
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Refer to demonstration at the top of page.
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