Figure 1.
Figure 2.
Figure 3.
Comparison of stacking method and other methods in disciplinary classification_
| Algorithm | Macro-Precision | Macro-Recall | Macro-F1 |
|---|---|---|---|
| SVM | 0.81 | 0.69 | 0.74 |
| NB | 0.64 | 0.77 | 0.68 |
| LSTM | 0.67 | 0.65 | 0.66 |
| Stacking | 0.81 | 0.79 | 0.80 |
Discipline distribution of the number of papers in the CPCN dataset_
| Main category | Data volume of main category | First-level category | Data volume of first-level category |
|---|---|---|---|
| 07 Science | 1,917 | 0703 Chemistry | 1,334 |
| 0706 Atmospheric Sciences | 583 | ||
| 0805 Materials Science and Engineering | 736 | ||
| 0807 Power Engineering and Engineering Thermophysics | 1,008 | ||
| 0813 Architecture | 638 | ||
| 08 Engineering | 15,322 | 0817 Chemical Engineering and Technology | 5,309 |
| 0819 Mining Engineering | 767 | ||
| 0820 Oil and Gas Engineering | 1,008 | ||
| 0823 Transportation Engineering | 750 | ||
| 0828 Agricultural engineering | 2,055 | ||
| 0830 Environmental Science and Engineering | 3,051 |
Examples of multidisciplinary research problems_
| Multidisciplinary research problems | The first-level disciplines involved |
|---|---|
| Catalytic, Cracking, Hydrogenation | 0703 Chemistry, 0817 Chemical Engineering and Technology, 0820 Oil and Gas Engineering |
| Oxidation, Desulfurization, Catalytic | 0817 Chemical Engineering and Technology, 0820 Oil and Gas Engineering, 0830 Environmental Science and Engineering |
| Rare earths, Catalysts, Environmentally friendly | 0805 Materials Science and Engineering, 0820 Oil and Gas Engineering |
| Coal Combustion, Flue Gas, Distribution | 0817 Chemical Engineering and Technology, 0823 Transportation Engineering |
| Communities, Microorganisms, Carbon Sources | 0828 Agricultural Engineering, 0830 Environmental Science and Engineering |
Text pattern of abstracts and titles of scientific papers_
| Research objective | Abstract features | Abstractive title |
|---|---|---|
| US | Study/investigate/test + individual object + structure/state/performance | Research/analysis of the performance/characteristics of problem |
| SO | To address/tackle + problem + based on/utilizing + method + construct/propose/build | Study of problem based on method |
| EXP-S | Summarize/review/introduce + individual object + current status/progress | The current status/overview of research on problem |
| EXP-RG | Investigate/explore/analyze/discuss + the relationship/interaction mechanism/influence + multiple objects | The impact /mechanism of the problem |
Manual Evaluation Results_
| Research problem | Quantities |
|---|---|
| Multidisciplinary research problems | 34 |
| Single-discipline research problems | 16 |
Comparison of different methods for research objective classification_
| Algorithm | Macro-Precision | Macro-Recall | Macro-F1 |
|---|---|---|---|
| SVM | 0.85 | 0.84 | 0.84 |
| NB | 0.81 | 0.81 | 0.81 |
| Random forest | 0.77 | 0.75 | 0.75 |
| LSTM | 0.69 | 0.62 | 0.65 |
| FastText | 0.71 | 0.67 | 0.68 |
Comparison of abstractive title generation between BART and ChatGLM_
| Research Objective | Model | 1-Gram | 2-Gram | 3-Gram | BLEU | Exact Match | Unigram |
|---|---|---|---|---|---|---|---|
| US | ChatGLM | 0.560 | 0.462 | 0.371 | 0.402 | 0.182 | 0.417 |
| BART | 0.582 | 0.474 | 0.376 | 0.411 | 0.145 | 0.369 | |
| SO | ChatGLM | 0.612 | 0.494 | 0.387 | 0.440 | 0.299 | 0.441 |
| BART | 0.631 | 0.498 | 0.374 | 0.437 | 0.356 | 0.438 | |
| EXP-S | ChatGLM | 0.501 | 0.436 | 0.359 | 0.351 | 0.186 | 0.346 |
| BART | 0.597 | 0.502 | 0.413 | 0.436 | 0.233 | 0.422 | |
| EXP-RG | ChatGLM | 0.588 | 0.487 | 0.401 | 0.422 | 0.197 | 0.441 |
| BART | 0.610 | 0.509 | 0.422 | 0.428 | 0.201 | 0.434 | |
| ALL | BART | 0.577 | 0.463 | 0.372 | 0.408 | 0.203 | 0.367 |