# PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification
## 6 Conclusions and Future Work  
To facilitate research on fine-grained paper classification, we introduce PaperNet-Dataset Version 1.0, which consists of multi-modal data (texts and figures). It contains hierarchical categories, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran multiple well known mainstream models on the PaperNet-Dataset. They performed poorly in the fine-grained tasks, never reaching an accuracy of 80%. In addition, we propose a multi-modal fusion method that increases the accuracy but still not satisfactory. The results show that there is plenty of room for improvement in fine-grained classification and PaperNet could be used as a benchmark dataset. We plan to expand PaperNet in future versions and hope that it will inspire more work in this challenging area of fine-grained paper classification.  
Conceptualization, T.Y.; Data curation, X.S. and J.Q.; Formal analysis, T.Y.; Funding acquisition, Z.H.; Investigation, T.Y. and Z.F.; Methodology, T.Y.; Project administration, T.Y.; Resources, Y.L. and Z.H.; Software, T.Y.; Supervision, Y.L. and Z.H.; Validation, T.Y.; Visualization, T.Y.; Writing--original draft preparation, T.Y.; Writing--review and editing, Y.L. and Z.H. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper was supported by the BUPT innovation and entrepreneurship support program (2022-YC-S002) and the Beijing Key Laboratory of Work Safety and Intelligent Monitoring Foundation.  
The authors declare no conflict of interest.  
\begin{table}
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**Dataset** & **Metrics** & **Naive Bayes** & **Adaboost** & **KNN** & **SVM** & **Random Forest** \\ \hline PaperNet\_2 & Precision & 97.91 \(\pm\) 0.65 & 96.51 \(\pm\) 0.56 & 96.87 \(\pm\) 0.63 & 99.12 \(\pm\) 0.62 & 97.23 \(\pm\) 0.12 \\  & Recall & 97.20 \(\pm\) 0.46 & 96.67 \(\pm\) 0.32 & 96.42 \(\pm\) 0.26 & 98.94 \(\pm\) 0.33 & 97.58 \(\pm\) 0.26 \\  & F1-score & 97.56 \(\pm\) 0.23 & 96.66 \(\pm\) 0.25 & 96.64 \(\pm\) 0.21 & 98.96 \(\pm\) 0.16 & 97.42 \(\pm\) 0.22 \\  & Accuracy & 96.44 \(\pm\) 0.36 & 94.08 \(\pm\) 0.23 & 93.56 \(\pm\) 0.43 & 96.98 \(\pm\) 0.26 & 96.32 \(\pm\) 0.16 \\ \hline PaperNet\_20 & Precision & 70.75 \(\pm\) 1.31 & 79.15 \(\pm\) 0.39 & 62.64 \(\pm\) 1.31 & 83.79 \(\pm\) 1.21 & 81.32 \(\pm\) 0.17 \\  & Recall & 43.98 \(\pm\) 0.63 & 78.36 \(\pm\) 0.32 & 55.89 \(\pm\) 0.75 & 66.72 \(\pm\) 0.69 & 72.15 \(\pm\) 0.08 \\  & F1-score & 46.92 \(\pm\) 0.39 & 78.46 \(\pm\) 0.16 & 58.01 \(\pm\) 0.87 & 69.97 \(\pm\) 0.52 & 76.46 \(\pm\) 0.09 \\  & Accuracy & 50.22 \(\pm\) 0.59 & 71.76 \(\pm\) 0.26 & 53.01 \(\pm\) 0.68 & 69.43 \(\pm\) 0.31 & 72.89 \(\pm\) 0.21 \\ \hline PaperNet\_CV & Precision & 50.89 \(\pm\) 1.29 & 76.81 \(\pm\) 0.89 & 55.85 \(\pm\) 0.85 & 80.67 \(\pm\) 1.12 & 82.12 \(\pm\) 0.06 \\  & Recall & 32.85 \(\pm\) 0.64 & 70.02 \(\pm\) 0.49 & 49.33 \(\pm\) 0.64 & 58.67 \(\pm\) 0.62 & 75.43 \(\pm\) 0.15 \\  & F1-score & 34.11 \(\pm\) 0.67 & 65.33 \(\pm\) 0.28 & 51.22 \(\pm\) 0.47 & 60.98 \(\pm\) 0.21 & 78.64 \(\pm\) 0.08 \\  & Accuracy & 44.96 \(\pm\) 0.91 & 72.40 \(\pm\) 0.63 & 50.67 \(\pm\) 0.56 & 64.34 \(\pm\) 0.49 & 73.86 \(\pm\) 0.36 \\ \hline PaperNet\_NLP & Precision & 61.58 \(\pm\) 1.16 & 80.68 \(\pm\) 0.67 & 70.65 \(\pm\) 0.83 & 86.56 \(\pm\) 1.24 & 82.89 \(\pm\) 0.18 \\  & Recall & 41.43 \(\pm\) 0.87 & 81.29 \(\pm\) 0.56 & 64.55 \(\pm\) 0.65 & 68.85 \(\pm\) 0.72 & 80.25 \(\pm\) 0.16 \\  & F1-score & 43.81 \(\pm\) 0.6