{"id":"https://openalex.org/W2902167559","doi":"https://doi.org/10.1109/icacci.2018.8554848","title":"A Hybrid Approach of Text Summarization Using Latent Semantic Analysis and Deep Learning","display_name":"A Hybrid Approach of Text Summarization Using Latent Semantic Analysis and Deep Learning","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2902167559","doi":"https://doi.org/10.1109/icacci.2018.8554848","mag":"2902167559"},"language":"en","primary_location":{"id":"doi:10.1109/icacci.2018.8554848","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2018.8554848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113502205","display_name":"Chintan Shah","orcid":"https://orcid.org/0009-0008-1162-7745"},"institutions":[{"id":"https://openalex.org/I27674431","display_name":"Indian Institute of Technology Gandhinagar","ror":"https://ror.org/0036p5w23","country_code":"IN","type":"education","lineage":["https://openalex.org/I27674431"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Chintan Shah","raw_affiliation_strings":["Information Technology Dept., Gandhinagar Institute of Technology, Gandhingar, India"],"affiliations":[{"raw_affiliation_string":"Information Technology Dept., Gandhinagar Institute of Technology, Gandhingar, India","institution_ids":["https://openalex.org/I27674431"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027401580","display_name":"Anjali Jivani","orcid":"https://orcid.org/0000-0003-0034-5204"},"institutions":[{"id":"https://openalex.org/I110116418","display_name":"Maharaja Sayajirao University of Baroda","ror":"https://ror.org/01bx8ja67","country_code":"IN","type":"education","lineage":["https://openalex.org/I110116418"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anjali Jivani","raw_affiliation_strings":["Computer Science & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, India"],"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, India","institution_ids":["https://openalex.org/I110116418"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5113502205"],"corresponding_institution_ids":["https://openalex.org/I27674431"],"apc_list":null,"apc_paid":null,"fwci":0.4887,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.73857042,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2039","last_page":"2044"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.9016488790512085},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.82001793384552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6735224723815918},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.574410617351532},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4987609386444092},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4968612492084503},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46838003396987915},{"id":"https://openalex.org/keywords/rouge","display_name":"ROUGE","score":0.44414305686950684},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4209170937538147},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4101802706718445},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3982750177383423},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35810160636901855}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9016488790512085},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.82001793384552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6735224723815918},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.574410617351532},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4987609386444092},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4968612492084503},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46838003396987915},{"id":"https://openalex.org/C7888048","wikidata":"https://www.wikidata.org/wiki/Q7277427","display_name":"ROUGE","level":2,"score":0.44414305686950684},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4209170937538147},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4101802706718445},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3982750177383423},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35810160636901855},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icacci.2018.8554848","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2018.8554848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5400000214576721,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W15741624","https://openalex.org/W65738273","https://openalex.org/W116833153","https://openalex.org/W1767824798","https://openalex.org/W1902674502","https://openalex.org/W1939882552","https://openalex.org/W1988543050","https://openalex.org/W2004338309","https://openalex.org/W2015599215","https://openalex.org/W2054211469","https://openalex.org/W2055247046","https://openalex.org/W2079810998","https://openalex.org/W2088709281","https://openalex.org/W2088795315","https://openalex.org/W2112652686","https://openalex.org/W2133750501","https://openalex.org/W2143331230","https://openalex.org/W2158139315","https://openalex.org/W2158899491","https://openalex.org/W2164587673","https://openalex.org/W2316564661","https://openalex.org/W2558254968","https://openalex.org/W2952230511","https://openalex.org/W2962965405","https://openalex.org/W3036846224","https://openalex.org/W4285719527","https://openalex.org/W6604728385","https://openalex.org/W6606244218","https://openalex.org/W6640511754","https://openalex.org/W6679867102","https://openalex.org/W6683557909","https://openalex.org/W6683738474","https://openalex.org/W6779887223"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W4389760904","https://openalex.org/W2000777050","https://openalex.org/W4312318079","https://openalex.org/W2110921901","https://openalex.org/W2151695628","https://openalex.org/W402673672"],"abstract_inverted_index":{"In":[0,89,129],"the":[1,31,70,81,87,93,104,126,138,146,158,198],"current":[2],"scenario":[3],"of":[4,72,92,99,118],"Information":[5],"Technology,":[6],"excessive":[7],"and":[8,25,56,79,156,167,201],"vast":[9],"information":[10],"is":[11,18,33,52,62,101,134],"available":[12,181],"on":[13,37,83,160,163,175,182],"online":[14],"resources":[15],"but":[16],"it":[17],"not":[19],"always":[20],"easy":[21],"to":[22,103,124,191],"find":[23],"relevant":[24],"useful":[26],"information.":[27],"Along":[28],"this":[29],"issue,":[30],"paper":[32],"presented":[34],"a":[35,63,96,153],"method":[36,46,55,171],"extractive":[38],"single":[39],"document":[40],"text":[41],"summarization":[42],"using":[43,131,149],"Deep":[44],"Learning":[45],"-":[47],"Self-Organizing":[48],"Maps":[49],"(SOM)":[50],"which":[51,61,178,194],"an":[53,121],"unsupervised":[54],"Artificial":[57],"Neural":[58],"Networks":[59],"(ANN)":[60],"supervised":[64],"method.":[65],"The":[66,141,185],"work":[67],"involves":[68],"investigating":[69],"effect":[71],"adding":[73],"mapped":[74],"sentences":[75],"from":[76,197],"SOM":[77,100],"visualization,":[78],"re-training":[80],"inputs":[82],"ANN":[84,105],"for":[85,137],"ranking":[86],"sentences.":[88],"individual":[90],"experiment":[91],"hybrid":[94,147],"model,":[95],"different":[97,176],"mapping":[98,150],"added":[102],"network":[106],"as":[107],"input":[108,139],"vector.":[109,140],"Hybrid":[110],"model":[111,148,200],"uses":[112],"Stochastic":[113],"Gradient":[114],"Descent":[115],"update":[116],"set":[117],"parameters":[119],"in":[120],"iterative":[122],"manner":[123],"minimize":[125],"cost":[127],"function.":[128],"addition,":[130],"back-propagation":[132],"weight":[133],"being":[135],"adjusted":[136],"empirical":[142],"results":[143],"show":[144],"that":[145],"clearly":[151],"provides":[152],"comprehensive":[154],"result":[155],"improves":[157],"F-score":[159],"average":[161],"5%":[162],"ROUGE-1,":[164],"ROUGE-2,":[165],"ROUGE-L":[166],"ROUGE-SU4.":[168],"This":[169],"novel":[170],"has":[172,188],"been":[173,189],"implemented":[174],"documents,":[177],"are":[179,195],"publicly":[180],"Opinosis":[183],"Dataset.":[184],"ROUGE":[186],"toolkit":[187],"used":[190],"evaluate":[192],"summaries":[193],"generated":[196,207],"proposed":[199],"other":[202],"existing":[203],"algorithms":[204],"versus":[205],"human":[206],"summary.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
