Keyword Acquisition for Language Composition Based on TextRank Automatic Summarization Approach

It is important to extract keywords from text quickly and accurately for composition analysis, but the accuracy of traditional keyword acquisition models is not high. Therefore, in this study, the Best Match 25 algorithm was first used to preprocess the compositions and evaluate the similarity between sentences. Then, TextRank was used to extract the abstract, construct segmentation and named entity model, and finally verify the research content. The results show that in the performance test, the Best Match 25 similarity algorithm has higher accuracy, recall rate and F1 value, the average running time is only 2182ms, and has the largest receiver working characteristic curve area, which is significantly higher than other models, reaching 0.954. The accuracy of TextRank algorithm is above 90%, the average accuracy of 100 text analysis is 94.23%, the average recall rate and F1 value are 96.67% and 95.85%, respectively. In comparison of the application of the four methods, the research model shows obvious advantages, the average keyword coverage rate is 94.54%, the average processing time of 16 texts is 11.29 seconds, and the average 24-hour memory usage is only 15.67%, which is lower than the other three methods. The experimental results confirm the superiority of the model in terms of keyword extraction accuracy. This research not only provides a new technical tool for language composition teaching and evaluation, but also provides a new idea and method for keyword extraction research in the field of natural language processing.

@article{Jiang2024,
  title     = {Keyword Acquisition for Language Composition Based on TextRank Automatic Summarization Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yan Jiang and Chunlin Xiang and Lingtong Li},
  doi       = {10.14569/IJACSA.2024.01504101},
  url       = {https://doi.org/10.14569/IJACSA.2024.01504101}
}