Jain, ArtiArora, AnujaMorato Lara, Jorge LuisYadav, DivakarKumar, Kumar Vimal2023-10-242023-10-242022-06-01Jain, A.; Arora, A.; Morato, J.; Yadav, D.; Kumar, K.V. Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm. Appl. Sci. 2022, 12, 6584. https://doi.org/10.3390/app121365842076-3417https://hdl.handle.net/10016/38657In the present scenario, Automatic Text Summarization (ATS) is in great demand to address the ever-growing volume of text data available online to discover relevant information faster. In this research, the ATS methodology is proposed for the Hindi language using Real Coded Genetic Algorithm (RCGA) over the health corpus, available in the Kaggle dataset. The methodology comprises five phases: preprocessing, feature extraction, processing, sentence ranking, and summary generation. Rigorous experimentation on varied feature sets is performed where distinguishing features, namely- sentence similarity and named entity features are combined with others for computing the evaluation metrics. The top 14 feature combinations are evaluated through Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure. RCGA computes appropriate feature weights through strings of features, chromosomes selection, and reproduction operators: Simulating Binary Crossover and Polynomial Mutation. To extract the highest scored sentences as the corpus summary, different compression rates are tested. In comparison with existing summarization tools, the ATS extractive method gives a summary reduction of 65%.eng© 2022 by the authors.Atribución 3.0 Españaautomatic text summarizationextractive summaryfeature sethindi languageHindihealtd datanamed entityreal coded genetic algorithmrouge metricsummarization toolresearch articleInformáticattps://doi.org/10.3390/app12136584open access11323Applied Sciences12AR/0000031148