# Improved Text Summarization of News Articles Using GA-HC and PSO-HC
## 2 Related Work  
The literature review is devoted into three subsections according to document type whether it is single document or multidocument. The authors in [12] discussed k mean clustering for text summarization. Moreover, scores are assigned to cluster on the bases of APRIORI probability. Finally, the sentence with high score is selected for summarization. An automatic extractive text summarization approach using genetic algorithm is proposed in [13] for optimizing the features scores and applied fuzzy logic for score assignment to all the sentences. Finally, high scored sentences are selected and presented in the summary. The model in [14] considered the text rank algorithm for text summarization. A Cascading Style Sheet (CSS) property for web designers is also introduced which can reduce the lengthy text on smaller screens with the help of text summarization. In [15], word-sentence relation with unsupervised graph ranking is proposed. The model integrates intrinsic value of words and sentences with good accuracy. In [16], the proposed ensemble model makes use of parallel ensemble approach with classification performed on voting system for text summarization. A bug report text summarization technique is presented in [17]. The model applied fuzzy c-mean clusters for similar sentences and fuzzy logic for making decision of adding or discarding sentences for final summary. The text summarization technique discussed in [18] investigated semantic and statistical features for summarization of text. The model used Word2 Vec for extracting semantics and K-means for grouping similar sentences in addition, ranked all the sentences and top \(n\) ranked sentences are considered as document summary. However, other clustering techniques such as fuzzy c mean and hierarchal clustering are not considered. An Arabic single document text summarization model is presented in [19]. The authors presented two text summarization approaches: one is scored based approach and other is binary classifier approach. The binary classifier is trained to predict whether the sentence is a part of final summary or not. An adaptive and Knowledge-based Event-index (KB-EI) cognitive model is introduced in [20]. The model applied cognitive based process on human memory and emotions for text summarization task. The model has learning phase for identification of information rich sentences and summarization phase for summarizing the document with important sentences. **Multi Document Summarization (MDS):**  
In [21], authors presented multi-document extractive text as a multi-objective optimization problem and proposed Artificial Bee Colony Optimization (ABC) algorithm to generate text summary. The authors in [22], applied Recursive Neural Network (RNN) for extorting images present in document and employed logistic classifier for finding probability of each sentence present in document for generating the final summary. In [23], PSO is applied on discrete and continues vector space, and sentimental analysis is used for removing redundancy. The model discussed in [24] considered Shark Smell Optimization (SSO) algorithm for summarization of multiple documents. SSO is investigated for optimizing the weights of the features extracted which are used for document summarization. The authors in [25] presented a text summarization model based on centroid technique and sentence embedding. An abstractive text summarization model is used in [26]. The model uses Generative Adversarial Network (GAN) with time decay attention mechanism for selection of important sentences and summary generation. In [27], the authors proposed 27 rules for classification of text for summary generation using fuzzy logic. The authors in [28] proposed fuzzy logic for identification and mapping of overlapping words. For overcoming duplicate sentence issue in text document, the proposed approach considered graph-based technique for generating summary. An extractive single document text summarization technique is discussed in [29].