RT Journal Article
T1 Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm
A1 Jain, Arti
A1 Arora, Anuja
A1 Morato Lara, Jorge Luis
A1 Yadav, Divakar
A1 Kumar, Kumar Vimal
AB In 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%.
PB MDPI
SN 2076-3417
YR 2022
FD 2022-06-01
LK https://hdl.handle.net/10016/38657
UL https://hdl.handle.net/10016/38657
LA eng
DS e-Archivo
RD 18 jul. 2024