{"id":"https://openalex.org/W4385482666","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191588","title":"GAE-ISUMM: Unsupervised Graph-based Summarization for Indian Languages","display_name":"GAE-ISUMM: Unsupervised Graph-based Summarization for Indian Languages","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385482666","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191588"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191588","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","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/A5089168458","display_name":"Lakshmi Sireesha Vakada","orcid":"https://orcid.org/0000-0002-8398-576X"},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Lakshmi Sireesha Vakada","raw_affiliation_strings":["IIIT Hyderabad"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT Hyderabad","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016034995","display_name":"Anudeep Ch","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anudeep Ch","raw_affiliation_strings":["IIIT Hyderabad"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT Hyderabad","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062052248","display_name":"Mounika Marreddy","orcid":"https://orcid.org/0000-0003-1184-640X"},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mounika Marreddy","raw_affiliation_strings":["IIIT Hyderabad"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT Hyderabad","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029606497","display_name":"Subba Reddy Oota","orcid":"https://orcid.org/0000-0002-5975-622X"},"institutions":[{"id":"https://openalex.org/I4210131512","display_name":"Centre Inria de l'universit\u00e9 de Bordeaux","ror":"https://ror.org/03tjcj052","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210131512"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Subba Reddy Oota","raw_affiliation_strings":["Inria Bordeaux,France","Inria Bordeaux, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inria Bordeaux,France","institution_ids":[]},{"raw_affiliation_string":"Inria Bordeaux, France","institution_ids":["https://openalex.org/I4210131512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038314215","display_name":"Radhika Mamidi","orcid":"https://orcid.org/0000-0003-0171-0816"},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Radhika Mamidi","raw_affiliation_strings":["IIIT Hyderabad"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT Hyderabad","institution_ids":["https://openalex.org/I65181880"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3263,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64292937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9998000264167786,"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":0.9995999932289124,"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":0.9524946212768555},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8429597616195679},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6545564532279968},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6210178136825562},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5423920154571533},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.49898576736450195},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4862140715122223},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4742140769958496},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.43976473808288574},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35621458292007446},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1133388876914978}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9524946212768555},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8429597616195679},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6545564532279968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6210178136825562},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5423920154571533},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.49898576736450195},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4862140715122223},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4742140769958496},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.43976473808288574},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35621458292007446},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1133388876914978},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191588","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7200000286102295,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W100656083","https://openalex.org/W1525595230","https://openalex.org/W1974339500","https://openalex.org/W1975579663","https://openalex.org/W1979068940","https://openalex.org/W2110693578","https://openalex.org/W2138621090","https://openalex.org/W2160204597","https://openalex.org/W2165874743","https://openalex.org/W2568676725","https://openalex.org/W2597979293","https://openalex.org/W2600702321","https://openalex.org/W2612675303","https://openalex.org/W2714275525","https://openalex.org/W2735674392","https://openalex.org/W2896457183","https://openalex.org/W2927490800","https://openalex.org/W2950635152","https://openalex.org/W2952138241","https://openalex.org/W2964015378","https://openalex.org/W2964144561","https://openalex.org/W3015621111","https://openalex.org/W3034961030","https://openalex.org/W3035390927","https://openalex.org/W3042942426","https://openalex.org/W3099919888","https://openalex.org/W3100124323","https://openalex.org/W3101864927","https://openalex.org/W3105230369","https://openalex.org/W3154261671","https://openalex.org/W3155992948","https://openalex.org/W3169483174","https://openalex.org/W3171978172","https://openalex.org/W3173360659","https://openalex.org/W3197314979","https://openalex.org/W3202379861","https://openalex.org/W4206053224","https://openalex.org/W4225112709","https://openalex.org/W4285289037","https://openalex.org/W4312752123","https://openalex.org/W4322614756","https://openalex.org/W4385245566","https://openalex.org/W6604150846","https://openalex.org/W6631501603","https://openalex.org/W6684578312","https://openalex.org/W6726873649","https://openalex.org/W6730084236","https://openalex.org/W6737479944","https://openalex.org/W6755207826","https://openalex.org/W6769263558","https://openalex.org/W6776232423","https://openalex.org/W6784577980","https://openalex.org/W6801157876","https://openalex.org/W6910546390"],"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/W2150160875","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000"],"abstract_inverted_index":{"Document":[0],"summarization":[1,17,39,65,98,117],"aims":[2],"to":[3,83,101,119],"create":[4],"a":[5,11,26,88,95],"precise":[6],"and":[7,30,35,53,87,156,162,178],"coherent":[8],"summary":[9,90],"of":[10,123,127,160,172],"text":[12,71,85],"document.":[13],"Many":[14],"deep":[15],"learning":[16],"models":[18,34,40],"are":[19,45],"developed":[20],"mainly":[21],"for":[22,41],"English,":[23],"often":[24,46],"requiring":[25],"large":[27],"training":[28],"corpus":[29],"efficient":[31],"pre-trained":[32],"language":[33,116],"tools.":[36],"However,":[37],"English":[38],"low-resource":[42],"Indian":[43,115,131],"languages":[44,132],"limited":[47],"by":[48],"rich":[49],"morphological":[50],"variation,":[51],"syntax,":[52],"semantic":[54],"differences.":[55],"In":[56,73],"this":[57],"paper,":[58],"we":[59,108],"propose":[60],"GAE-ISUMM,":[61],"an":[62],"unsupervised":[63],"Indic":[64],"model":[66,105,168],"that":[67],"extracts":[68],"summaries":[69],"from":[70],"documents.":[72],"particular,":[74],"our":[75,104,176],"proposed":[76,167],"model,":[77],"GAE-ISUMM":[78,128],"uses":[79],"Graph":[80],"Autoencoder":[81],"(GAE)":[82],"learn":[84],"representations":[86],"document":[89],"jointly.":[91],"We":[92,174],"also":[93],"provide":[94],"manually-annotated":[96],"Telugu":[97],"dataset":[99,177],"TELSUM,":[100,155],"experiment":[102],"with":[103,110],"GAE-ISUMM.":[106,124],"Further,":[107],"benchmark":[109,152],"the":[111,121,134,158,166,170],"most":[112],"publicly":[113],"available":[114],"datasets":[118],"investigate":[120],"effectiveness":[122],"Our":[125],"experiments":[126],"on":[129,146,154],"seven":[130],"make":[133],"following":[135],"observations:":[136],"(i)":[137],"it":[138,150],"is":[139],"competitive":[140],"or":[141],"better":[142],"than":[143],"state-of-the-art":[144],"results":[145,153],"all":[147],"datasets,":[148],"(ii)":[149],"reports":[151],"(iii)":[157],"inclusion":[159],"positional":[161],"cluster":[163],"information":[164],"in":[165],"improved":[169],"performance":[171],"summaries.":[173],"open-source":[175],"code":[179],"<sup":[180,183],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[181,184],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[182,185],"https://github.com/scsmuhio/Summarization.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}