# Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm
## 2 Related Work
### ATS for the Hindi Language

Table 1: _Cont._features, e.g., overall sentence position, length of sentence, and occurrence of inverted commas for the generation of the textual summary. [PERSON] and [PERSON] (2019) mention a rule-based approach for the Hindi text summarization. They use deadwood removal to generate the summary, where deadwood refers to a word or phrase which carries no meaning in the summarization task. Their system gives 96% accurate results and a 60-70% compression rate when tested on 30 different documents. However, their system does not consider the semantics of the text. [PERSON] and [PERSON] (1998) propose the Bernoulli model of randomness for the Hindi text summarization. They develop a graph-based ranking algorithm that computes an informative measure for each document term and generates indexing values for each word within the document. These indexes are used to select words for the summary. [PERSON] and [PERSON] (2018) discuss Support Vector Machine (SVM) based single document summarization for the Hindi language. They divide the sentences into 4 categories-- most influential, important, less important, and insignificant. The experiments are carried out on news stories from categories such as Bollywood, politics, and sports. Their results show 72% accuracy at a compression ratio of 50%, and 60% accuracy at a compression ratio of 25%. [PERSON] et al. (2018) propose a back-propagation based on multiple Hindi textual document summarization systems. They use features such as sentence length, the position of the sentence, the similarity of the sentence, and others. Among the 70 chosen documents, they use 50 documents to train the network, while the remaining 20 documents are for testing purposes. [PERSON] and [PERSON] (2019) propose bio-inspired computing for the Hindi text summarization. They use semantic graphs and particle swarm optimization algorithms. The semantic graph captures the semantic structure of the textual document. The particle swarm optimization searches for the optimal solution irrespective of the large dimensional space. They achieve precision (42.86%), recall (60%), and F-measure (50.01%), respectively. [PERSON] et al. (2019) work with a genetic algorithm for the Hindi ATS system while collecting online news articles from Dainik Bhaskar, Patrika, and Zee news where 15 text documents are of categories like Bollywood, politics, sports, science, and history. They used nine features; among them, 6 are statistical features (TF-ISF, sentence length, sentence position, numerical data, sentence to sentence similarity, and title word), and 3 are linguistic features (subject-object-verb qualification, subject similarity, and adjective feature). In this work evaluation measures are computed at different compression rates of 25%, 50%, and 60%, respectively. [PERSON] and [PERSON] (2019) develop an extractive lexical knowledge-rich topic modeling text summarizing approach. The tagged Latent Dirichlet Allocation (LDA) modeling is applied over 114 Hindi novels including short stories from [PERSON]'s stories. The system performs preferably to the baseline algorithms for 10-30% compression ratios and given evaluation metrics. However, semantic features are not taken into consideration and hence, results are somewhat declined when compared with baseline systems for different compression rates. [PERSON] et al. (2019) provide a comprehensive review of automatic text summarization methods. They discuss that automatic text summarization is difficult and demanding for the Hindi language and is still an unsolved topic due to lack of corpus and insufficient processing tools.