# Improved Text Summarization of News Articles Using GA-HC and PSO-HC
## 3 Propose Model
### Pre-Processing
The data preprocess involved in our proposed model is removing URLs and stop words, tokenization of words and sentences, lemmatization of words, extraction of proper nouns, extraction of verb phrases and noun phrases. NLTK tool kit is used for preprocessing the documents. ### Evaluation Method

We used ROUGE measures for evaluation of proposed model. ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. ROUGE was proposed by [PERSON] in 2004 [49]. ROUGE evaluates the summaries generated by the machine learning model with the summaries created by humans. ROUGE evaluates these summaries by counting overlapping basic units such as word sequence, word pairs and \(n-\)gram grammar between the generated summaries and reference summaries. ### Comparison with Other Algorithms

To evaluate the performance of proposed algorithm, we considered two clustering algorithms and one optimization algorithm identified from literature that is as follow:

Clustering Algorithms:

The two clustering algorithms are as follow;

K-Means: numerous text summarization models presented in literature used K-Means algorithm [50] for grouping similar sentences from the document. Fuzzy C-Means: in text summarization models in literature used Fuzzy C-Means [51] in their work for making clusters of similar sentences. Comparison with Other Methods. Semantic Text Summarizer [18] model is considered for comparison because same preprocessing is used in [18]. PKUSUMSUM [52] is a Java platform-based summarization model. PKUSUMSUM summarize multiple languages text. The model has the ability to summarize single document, multi-document and topic-based multi document summarization. TextRank [53] is a graph-based model presented in 2004. It generates a graph of the given text data in which the sentences are represented as a vertex. OPINOSIS [54] is a graph-based text summarization model which is capable of creating short and concise summaries of the given text document. OPINOSIS generates abstractive summaries of given text documents. Weighted ATS [55] is a weighted word vector representation method for TF-IDF for ATS. The proposed model is a presumptive way for capturing all possible semantic meanings from text, as well as statistical and linguistic aspects, for large data on the internet. By distinguishing semantically distinct sentences, the proposed word vectors help to improve the diversity of the resulting summary. ### Experimental Result

For model evaluation purposes all the documents from the dataset are summarized using proposed models and two understudied models. Summaries are passed to Rouge 1, Rouge 2, Rouge 3, and Rouge L. The average results for each Rouge value (precision, recall, and F1-score) are presented of all the summaries. Figure 5a-c represents the average precision, average recall, and F1 score, respectively, for DUC dataset. These figures represent the simulations results of proposed algorithms

GA-HC and PSO-HC with other understudied models. For evaluation purposes, the proposed algorithms GA-HC and PSO-HC are also compared for CNN/Daily mail dataset in Figure 6a-c for average precision, average recall, and F1 score, respectively. The results are compared with understudied algorithms. In all results, proposed algorithms performed efficiently in comparison with other algorithms due to exploration and exploitation capabilities of GA-HC and PSO-HC. Due to exploration capability proposed algorithms easily escape from local optimum values, and due to exploitation capability, achieves better optimum values. The GA-HC performed better than PSO-HC due to more exploration capability than PSO-HC. Figure 2.2 _Cont._

Figure 2.3 Comparison of various methods over DUC dataset for average values of (**a**) precision, (**b**) recall, and (**c**) F1-score. Figure 2.4 \(Cont.\)

Tables 1 and 2 showed the results of GA and PSO with three clustering approaches, respectively over DUC dataset. Experimental results demonstrate the proposed models out perfume other text summarization models in terms of precision, recall, and F1-score.