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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

  • Nougat api path can be installed using this link: install Nougat â€‹

(Recommend using pytorch GPU environment)

02

Visit the website

  • Click here to visit !

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03

Settings

  • Fill in your Openai api key and nougat path.

  • Click Save button.

04

Start to use

  • Upload your PDF paper files.

  • Wait for the analysis to complete.

  • Refer to demonstration at the top of page.

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