# Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm
## 2 Related Work
### Genetic Algorithm for Summarization  
The Genetic Algorithm [23; 24] is observed as an optimization function with a wide range of application domains. GA is applied to text clustering [25], query path optimization [26], pattern recognition [27], intrusion detection [28], and so on. Another wide usage of genetic algorithms is for the extractive text-based summarization task over available datasets for different languages while using statistical and linguistic features and to evaluate summarization measures (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Dataset(s)** & **Language(s)** & **Features Extraction** & **Evaluation Measures** \\ \hline \multirow{4}{*}{[PERSON] et al., 2010 [29]} & DUC 2002\(-\)533 news & \multirow{4}{*}{English} & \multirow{4}{*}{31 sentence scoring metrics} & ROUGE-1 \\  & articles, Haaretz & & & English \\  & newspaper\(-\)50 news & & Hebrew & & ROUGE-1 \\  & articles & & & & \\ \hline \multirow{4}{*}{[PERSON] et al., 2011 [30]} & DUC & \multirow{4}{*}{English} & title feature, sentence length, & \multirow{4}{*}{Precision: 49.80\%} \\  & & & term weight, sentence position, & & Recall: 44.64\% \\  & 2002\(-\)100 documents & & proper noun, numerical data, & & F-score: 46.62\% \\  & & & thematic word & & \\ \hline \multirow{4}{*}{[PERSON] et al., 2012 [31]} & DUC & \multirow{4}{*}{English} & title feature, sentence length, & \multirow{4}{*}{ROUGE-1} \\  & 2002\(-\)100 documents & & & & \\ \cline{1-1}  & & & & thematic words & \\ \hline \multirow{4}{*}{[PERSON] and [PERSON], 2013 [32]} & DUC 2002\(-\)567 news & \multirow{4}{*}{English} & \multirow{4}{*}{n-gram, frequency of words, sentence position} & \multirow{4}{*}{F-score (48.27\%)} \\  & 2013 [32] & articles & & & \\ \hline \multirow{4}{*}{[PERSON] and [PERSON], 2013 [33]} & \multirow{4}{*}{-} & \multirow{4}{*}{Hindi} & statistical features TF-ISF, & \multirow{4}{*}{Precision} \\  & & & sentence length, sentence & \\ \cline{1-1}  & & & position, numerical data, & \\ \cline{1-1}  & & sentence-to-sentence similarity, & & \\ \cline{1-1}  & & title feature, linguistic features: & & \\ \cline{1-1}  & & SOV qualification, & & \\ \cline{1-1}  & & subject similarity & & \\ \hline \hline \multirow{4}{*}{[PERSON] et al., 2015 [34]} & \multirow{4}{*}{-} & \multirow{4}{*}{Hindi} & statistical features TF-ISF, & \multirow{4}{*}{Suggest ROUGE (unspecified)} \\  & & & sentence length, sentence & \\ \cline{1-1}  & & sentence-to-sentence similarity, & & \\ \cline{1-1}  & & title word, linguistic features: & & \\ \cline{1-1}  & & proper noun, thematic words & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Genetic algorithm based extractive text summarization task.  
It has been observed in the literature study phase that text summarization research for Hindi or other languages generally uses a genetic algorithm [43]. Feature generation discretizes the real-valued features and this could be the reason for achieving poor performance in the existing works, whereas actual quantitative feature values can help in improving performance [44]. Henceforth, a real-valued/coded genetic algorithm is explored and applied for text summarization of the Hindi Language by making use of generated features.