# Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm
## 1 Introduction  
Automatic Text Summarization (ATS) [1, 2] is a process to generate a summary while preserving the essence, by eliminating irrelevant or redundant content from the text. ATS provides vital information in a much shorter version, usually reduced to less than half of the length of the input text. It remedies the challenge of information overload and helps in information retrieval tasks. ATS provides concise information with reduced redundancy [3] in an effective manner related to news articles [4], emails, official government documents, and many more. In generality, ATS utilizes either an extractive summary [5] or an abstractive summary [6]. An extractive summary is generated while selecting essential sentences from the given textual document. The sentence selection criteria are based on the text's statistical parameters and linguistic features to combine those sentences into the final summary. On the other hand, an abstractive summary is generated by considering into the more profound understanding of semantics for the given textual document. It uses diversified linguistic features to examine and interpret the text and generate new sentences. In this research, we have worked upon ATS for the Hindi language using an extractive strategy over the Hindi Health Data (HHD) corpus, which is available in the Kaggle datasets (Section 4.1). Hindi is written in the Devanagari script [7] and is an official language of India along with English. Although much work is available on the English text summarization [1; 5; 6; 8; 9], comparatively, lesser research is performed in the case of the Hindi language. Hindi serves as a native language for most people living in the north-central states of India- Himachal Pradesh, Uttarakhand, Haryana, Delhi, Uttar Pradesh, Chhattisgarh, Madhya Pradesh, Jharkhand, Bihar, and Rajasthan. It is the third most spoken language globally [10], the mother tongue of 343.0 million people, and the second language for 258.3 million people. Moreover, Hindi is spoken in many countries outside India (Trinidad and Tobago, Guyana, Fiji, Suriname, Mauritius, and Singapore). The extensive Hindi speaking population has motivated the focus of this research on this language. An extractive summarization is supported by pre-processing, feature extraction, and processing phases. In this study, the pre-processing phase includes sentence segmentation, word tokenization, stemming, Part-of-Speech (POS) tagging, and stop-words removal. The feature extraction phase includes eight features: sentence paragraph position, numerical data, sentence length, keywords within a sentence, sentence similarity, Named Entities (NEs) [11; 12], English-Hindi words within a sentence, and Term Frequency (TF)-Inverse Sentence Frequency (ISF). These features influence the importance of sentences using the weighted-learning concept so that final sentence scores are calculated using feature weights. The size and complexity of generated features is a problem for ATS processing of the Hindi language. To reduce the dimensionality problem of generated feature data, an optimization algorithm is requisite which can explore the features search space to exploit the best feature set for text summarization. Although automatic text summarization for the English language has worked on numerous optimization algorithms, Hindi Language automatic text summarization needs systematic consideration. The generated features using the mentioned steps above contain real values. To deal with real values of generated features, [PERSON] et al. [13] suggested and validated a real-valued Genetic Algorithm (GA) for Natural Language Processing (NLP) [14] research problems. The idea to apply a real coded genetic algorithm is taken from [PERSON] et al. [15]. The processing phase considers the Real Coded Genetic Algorithm (RCGA), which search capability explores the best feature weights and differentiates significant features. The feature combinations have been experimented with over the test dataset for different compression rates.