# Improved Text Summarization of News Articles Using GA-HC and PSO-HC
## 3 Propose Model
### Pre-Processing
There are two approaches in hierarchical clustering as shown in Figure 2.

In hierarchical agglomerative clustering (HAC), all points are individual clusters initially. The individual clusters combine to make big clusters on the bases of distance, and this keeps on repeating until all data points are in one big cluster also known as bottom-up approach. On the other hand, the divisive clustering approach (DCA) is opposite to HCA. In (DCA) initially there is one big cluster, and it keeps on dividing clusters until all data points are individual clusters also known as top-bottom approach. Our model used HAC for grouping similar sentences together. ### Ranking Algorithm

The ranking model employed multiple statistical features for ranking each sentence from the clusters and then these extracted values are normalized. Modified genetic algorithm is used for optimizing the scores of the features and finally the sum of all the optimized features scores constitutes the rank of that sentence. The statistical features used by out model are as follow;

Sentence Length:

According to [42], the length of sentence is connected to the importance of sentence. The proposed model considers sentence length as one of the features for ranking the sentences. TF-IDF Score:

Term Frequency Inverse Document Frequency (TF-IDF) is one of the most important and widely used feature in entire literature of text summarization. TF-IDF plays an important role in identification of most important words goes through the text document. These words can help in extraction of important sentences form the document. TF-IDF is expressed as

\[s_{i}^{t\ f}=\sum_{w\bigtriangledown\ s_{i}}t_{f}\ (w) \tag{1}\]

where \(s_{i}^{t\ f}\) is \(i\)th sentence's sum of all tf-idf score of words in that sentence, \(t_{f}\ (w)\) is a function that returns TF-IDF score of a word \(w\). Sentence Position:

The most important sentences in the news document are located in the beginning and at the end of the document [42; 43]. The position of sentence is calculated as follows:

\[s_{i}^{p}=1-\frac{s_{i}-1}{|S|} \tag{2}\]

where \(s_{i}^{p}\) is score of sentence position for \(i\)th sentence form the given input document and \(|S|\) represent the cardinality of sentences set \(S\) of whole document. Figure 2: Hierarchical Clustering. Noun Phrases and Verb Phrases:

The sentences which have high number of noun phrases and verb phrases contains good amount of information [44]. We used Stanford POS Tagger for extraction of noun phrases and verb phrases from each sentence in the document. Proper Noun:

The sentences which contain proper nouns hold a lot of information and is important for text summarization of the document [45]. Aggregate Cosine Similarity:

Cosine similarity is used for computing similarity between two vectors. The work conducted by [46] proposed that cosine similarity effective in important sentence extraction. Many text summarization models proposed in literature use cosine similarity feature for extracting important sentences. The cosine similarity can be calculated as

\[s_{i}^{c}=\frac{\sum_{j=1,\ \ j\
eq i}^{|S|}c\left(s_{i},s_{j}\right)}{|S|} \tag{3}\]

where \(s_{i}^{c}\) is aggregate cosine similarity score ith sentence, \(c\left(s_{i},s_{j}\right)\) indicates cosine similarity between \(s_{i}\) and \(s_{j}\). System model flow diagram shown in Figure 3.

### Genetic Algorithm (GA)

GA is inspired from the bio evolution of species [47]. Text summarization is considered as NP-hard problem and GA has proved to be effective in solving NP-hard problem. Recently evolutionary algorithms havegained popularity in the domain of text mining and more specifically in text summarization. A lot of text summarization models presented in literature are using GA to optimize the weights of sentences and identification of important sentences. GA is slow in convergence. However, GA is robust and produced best possible optimized results over several generations of population by applying mutation and crossover operators.