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This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning,\n            <jats:bold>Latent Semantic Analysis (LSA),<\/jats:bold>\n            neural networks, cluster, and fuzzy logic. In this paper, a semantic graph-based approach\n            <jats:bold>SGATS (Semantic Graph-based approach for Automatic Text Summarization)<\/jats:bold>\n            is proposed to generate an extractive summary. The proposed approach constructs a semantic graph of the original Hindi text document by establishing a semantic relationship between sentences of the document using Hindi Wordnet ontology as a background knowledge source. Once the semantic graph is constructed, fourteen different graph theoretical measures are applied to rank the document sentences depending on their semantic scores. The proposed approach is applied to two data sets of different domains of Tourism and Health. The performance of the proposed approach is compared with the state-of-the-art TextRank algorithm and human-annotated summary. The performance of the proposed system is evaluated using widely accepted ROUGE measures. The outcomes exhibit that our proposed system produces better results than TextRank for health domain corpus and comparable results for tourism corpus. Further, correlation coefficient methods are applied to find a correlation between eight different graphical measures and it is observed that most of the graphical measures are highly correlated.\n          <\/jats:p>","DOI":"10.1145\/3464381","type":"journal-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T18:22:56Z","timestamp":1632162176000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["SGATS: Semantic Graph-based Automatic Text Summarization from Hindi Text Documents"],"prefix":"10.1145","volume":"20","author":[{"given":"Manju Lata","family":"Joshi","sequence":"first","affiliation":[{"name":"Banasthali Vidyapith, Banasthali, Rajasthan, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nisheeth","family":"Joshi","sequence":"additional","affiliation":[{"name":"Banasthali Vidyapith, Banasthali, Rajasthan, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Namita","family":"Mittal","sequence":"additional","affiliation":[{"name":"Malaviya National Institute of Technology, Jaipur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,9,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/089120105774321091"},{"key":"e_1_2_1_2_1","volume-title":"Gephi: An open-source software for exploring and manipulating networks. 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