# Improved Text Summarization of News Articles Using GA-HC and PSO-HC
## 1 Introduction  
The internet technology known as World Wide Web has seen a lot of advancements in last two decades. In current era internet is cheap and easily available all around the world. This gave rise to exponential growth of information [1]. Due to the presence of large number of users, different kinds of content creating and social networking organizations have turned their direction towards the internet in order to reach a much bigger audience. Even newspapers and news channels have started to adopt internet for news reporting and publishing news articles. It is cumbersome to read constantly uploaded web pages or articles every minute all around the globe. Moreover, it is not humanly possible to a read large amount of information. Users usually do not read entire web pages or articles, instead users just scan the entire pages or articles just to retrieve few sentences or parts of those sentences to obtain the main crux of the whole information in that article or web page [2]. With such a huge amount of information, it is difficult for the user to identify an important part orparts of sentences which hold the main crux of entire article in less time and with great precision and accuracy [3]. Text summarization is one of the most effective and simplest technique for giving the central idea or main information from the large amount of information. Manual text summarization has been used from early days when the only way to convey information was either through books or through newspapers. The manual text summarization may contain biasness and is time consuming with higher chances of error. Moreover, the accuracy and precision of summary documents not possible for large amount of data [4]. Natural Language Processing (NLP) expresses the interaction between human language and computer. Automatic text summarization is a subfield of NLP and addresses the issue of information retrieval from the data surrounded by redundant information with the help of machine learning. The text summary is generated in fewer amounts of time and with great precision and accuracy [5]. The text summarization with the help of machine learning was proposed by [PERSON] [3], the model extracted the abstract of the papers and presented them as text summary. In single document text summarization, a single document is assumed as input and short summarized paragraph is considered as output [6]. Multi-document text summarization was introduced after single document text summarization approach and is more complex than single document text summarization. The multi-document text summarization is similar to single document text summarization. However, the multi-document text summarization takes multiple documents as input and provide single summarized paragraph [7]. Text summarization techniques are also proposed in literature in terms of output. There are two types of text summarization techniques, one is extractive text summarization approach, and the other is abstractive text summarization technique. In extractive text summarization, the final summary considered the same sentences as they are provided in the input document only the important sentences are selected and joined in one paragraph and presented as extractive summary [8]. Extractive text summarization was the first one to be introduced in the domain of automatic text summarization [9]. The second type of approach for automatic text summarization is called abstractive text summarization. Abstractive text summarization uses the same methodology for identification and extraction of sentences but give output summary in different words and sentences conveying the same meaning [10]. Abstractive summarization is like a human writing the summary in its own words rather than using same sentences and words from the document. Abstractive text summarization came after extractive text summarization, and is more complex than extractive text summarization techniques [11]. There are many text summarization techniques are existing in literature.