# Improved Text Summarization of News Articles Using GA-HC and PSO-HC
## 3 Propose Model
### Pre-Processing  
The purpose of pre-processing stage is to eliminate inconsistency in the data and generate normalize data. For pre-processing, we used Stanford core NLP package [37]. The proposed model pre-possessing steps are as follow. Removing URL's: URLs existing in document are removed in pre-processing stage. Lower-Case: All the upper-case words are converted to lower-case, present inside the document. Figure 1: Proposed model overview. Remove Stop-Words: Stop words do not have any meaning in text summarization. Therefore, the stop-words are removed with the help of list of stop words provided by Stanford core NLP package. Tokenization: Tokenization is a process of splitting words into individual elements. Each sentence is tokenized into words before passing to the model. Lemmatization: The tokenized words are further reduced to their root word or stem word. B. Semantic Extraction  
The proposed model applied distributional semantic algorithm for extraction of semantics of words. The distributional semantic model does not depend on lexical and linguistic analysis. The distributional semantic model generates dynamic semantic in high dimensional vector space using statistical analysis of the various kinds of words. The distributional method creates semantic embedding with the help of statistical calculations of the environment in which word exists. The high dimensional real value vector of every word is computed and is presented in word-vector or word embedding form. Our proposed model applied Word2 Vec model [38] based on distributional semantic algorithm for extraction of semantics of words. Word2 Vec employed neural network model consisting of two-layers. The model takes text data as input and generates word vectors. Continuous Bag of Words (CBOW) and Skip-Gram are Word2 Vec approaches. Skip-Gram predicts context (surrounding words) of word from the word while CBOW predicts word from the context (surrounding words) of the word. The proposed model considered Skip-Gram approach of Word2 Vec. In this research work, the Word2 Vec model is pre-trained on Google news dataset. Word2 Vec has produced good results in extracting semantics as it has been used in many techniques. Word2 Vec is employed in [39] for bootstrapping to generate automatic annotated emotional corpus. Word2 Vec is used for solving the words sense in word sense disambiguation in [40]. Word2 Vec is applied by [41] for extraction of semantics of words for text coherence problem. C. Big Vectors  
Big vectors represent a sentence's rich semantic content and are based on the distribution hypothesis. It is a semantically comparable bag-of-words representation of a sentence comprising of semantically similar words in particular. These massive vectors of all sentences in a document are obtained. Because the number of words in a phrase varies, big vectors of various sizes are generated. Big vectors of sentences are created by joining similar words together obtained from Word2 Vec model. The big vectors of sentences are generated by summing all the words vectors present in the sentences shown in Algorithm 1. Using above mentioned methodology, big vectors are obtained for each sentence in the document. Since the number of words are not same in all sentences the size of big vectors is fixed to \(n\) number of dimensions. ```
1. Begin
2. Initialize with n number of clusters with each cluster containing one element
3. Calculate the least distance between pair of clusters
4. Calculate the most like pairs of the clusters
5. Update the distance matrix
6. Repeat from step 3 if there are more than one clusters are left
7. End

### Clustering

In this paper, clustering is used to group similar sentences in the document. Hierarchical clustering is employed in the proposed model. Hierarchical clustering considers each point as an individual cluster and all points as one cluster. Hierarchical clustering considers distance parameter to combine or divide each cluster.