# Improved Text Summarization of News Articles Using GA-HC and PSO-HC
## 3 Propose Model
### Pre-Processing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Genetic Algorithm (GA)**} \\ \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Rouge Type**} & **Hierarchical** & \multirow{2}{*}{**K-Means**} & **Fuzzy C-Means** \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{Avg. PR} & 1 & 0.99 & 0.88 & 0.81 \\  & 2 & 0.99 & 0.80 & 0.67 \\  & 3 & 0.98 & 0.78 & 0.64 \\  & L & 0.99 & 0.89 & 0.81 \\ \hline \multirow{4}{*}{Avg. Recall} & 1 & 0.48 & 0.44 & 0.42 \\  & 2 & 0.48 & 0.38 & 0.34 \\  & 3 & 0.47 & 0.37 & 0.32 \\  & L & 0.54 & 0.49 & 0.47 \\ \hline \multirow{4}{*}{Avg. F1-Score} & 1 & 0.63 & 0.56 & 0.54 \\  & 2 & 0.62 & 0.51 & 0.43 \\ \cline{1-1}  & 3 & 0.62 & 0.49 & 0.41 \\ \cline{1-1}  & L & 0.69 & 0.62 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Genetic Algorithm (GA) with clustering techniques. Figure 6: Comparison of various methods over CNN/daily mail dataset for average values of (**a**) precision, (**b**) recall, and (**c**) F1-score. ## 5 Conclusions

In this paper, two variants of automatic text summarization model are presented. The proposed approach employed distributional semantics of the words present in the sentences of the text and used hierarchical clustering technique for grouping similar sentences. GA-HC applied GA for optimizing the results of extracted features while PSO-HA used PSO for optimizing the results of extracted features. Finally, the top ranked sentences are selected on the basis of certain threshold and combined to make a summary. The position of sentences is kept same as they appeared in the original text. Our works can be concluded as: a. Applying underlying meaning of words and semantics as feature in text summarization to generate improved and better summaries. b. Hierarchical clustering technique can produce better results and c. Evolutionary techniques used for optimizing the features scores can be used to produce better summaries. In future work, human evaluation will be considered which can further strengthen model performance. Moreover, multiple aspects including readability, correctness, completeness, and compactness of documents can be considered to improve the quality of summary. Moreover, the deep learning models will be considered for the data extraction and optimized using metaheuristic techniques [56; 57; 58; 59; 60; 61; 62]. Conceptualization, M.M., S.L., and M.A.K. ; methodology, M.M., M.H., and S.L. ; software, M.M. and M.H. ; validation, M.A.K., U.T., and S.K. ; formal analysis, U.T. and S.K. ; investigation, U.T. and M.A.K. ; resources, H.-S.Y. and S.L. ; data curation, M.A.K. and H.-S.Y. ; writing--original draft preparation, M.M., S.L., and M.H. ; writing--review and editing, H.-S.Y., M.A.K. and J.-IC. ; visualization, J.-IC. ; supervision, M.A.K. and M.H. ; project administration, S.L. and J.-IC. ; funding acquisition, H.-S.Y. and J.-IC. All authors have read and agreed to the published version of the manuscript. This study was partially supported by Ewha Womans University. Not applicable. The CASIA b dataset is utilized for the experimental process. This dataset is publically available for research purpose. Here is the link of dataset: Center for Biometrics and Security Research ([[http://www.cbsr.ia.ac.cn/english/index.asp](http://www.cbsr.ia.ac.cn/english/index.asp)]([http://www.cbsr.ia.ac.cn/english/index.asp](http://www.cbsr.ia.ac.cn/english/index.asp)), accessed on 5 November 2021). The authors declare no conflict of interest in this work.