{"id":"https://openalex.org/W7130656132","doi":"https://doi.org/10.5121/csit.2026.160207","title":"A COMPREHENSIVE COMPARISON OF TEXT SUMMARIZATION PERFORMANCE: AMULTIFACETED EVALUATION OF LARG LANGUAGE MODELS WITH PRACTICALCONSIDERATIONS","display_name":"A COMPREHENSIVE COMPARISON OF TEXT SUMMARIZATION PERFORMANCE: AMULTIFACETED EVALUATION OF LARG LANGUAGE MODELS WITH PRACTICALCONSIDERATIONS","publication_year":2026,"publication_date":"2026-01-24","ids":{"openalex":"https://openalex.org/W7130656132","doi":"https://doi.org/10.5121/csit.2026.160207"},"language":"en","primary_location":{"id":"doi:10.5121/csit.2026.160207","doi":"https://doi.org/10.5121/csit.2026.160207","is_oa":true,"landing_page_url":"https://doi.org/10.5121/csit.2026.160207","pdf_url":"https://doi.org/10.5121/csit.2026.160207","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Science and Machine Learning","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5121/csit.2026.160207","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011968574","display_name":"Anantharaman Janakiraman","orcid":"https://orcid.org/0009-0008-3641-0788","observed_orcids":["https://orcid.org/0009-0008-3641-0788"]},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anantharaman Janakiraman","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science , Florida Atlantic University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science , Florida Atlantic University","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032994571","display_name":"Behnaz Ghoraani","orcid":"https://orcid.org/0000-0003-0075-7663","observed_orcids":["https://orcid.org/0000-0003-0075-7663"]},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Behnaz Ghoraani","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science , Florida Atlantic University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science , Florida Atlantic University","institution_ids":["https://openalex.org/I63772739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I63772739"],"apc_list":null,"apc_paid":null,"fwci":8.9362,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.94216432,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"72","last_page":"91"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9247999787330627,"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":0.9247999787330627,"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":0.060499999672174454,"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":0.004900000058114529,"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/text-summarization","display_name":"text summarization","score":0.9760000109672546},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9950000047683716},{"id":"https://openalex.org/keywords/summarization-evaluation","display_name":"summarization evaluation","score":0.7390000224113464},{"id":"https://openalex.org/keywords/factual-consistency","display_name":"factual consistency","score":0.9869999885559082},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"semantic similarity","score":0.9900000095367432},{"id":"https://openalex.org/keywords/lexical-overlap","display_name":"lexical overlap","score":0.9829999804496765},{"id":"https://openalex.org/keywords/human-evaluation","display_name":"human evaluation","score":0.6650000214576721},{"id":"https://openalex.org/keywords/model-efficiency","display_name":"model efficiency","score":0.45899999141693115},{"id":"https://openalex.org/keywords/cost-effectiveness","display_name":"cost-effectiveness","score":0.8080000281333923},{"id":"https://openalex.org/keywords/information-overload","display_name":"information overload","score":0.656000018119812}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9132000207901001},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7325000166893005},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6201000213623047},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5857999920845032},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5673999786376953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5649999976158142},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5566999912261963},{"id":"https://openalex.org/C186625053","wikidata":"https://www.wikidata.org/wiki/Q1130191","display_name":"Information overload","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32330000400543213},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C2779525943","wikidata":"https://www.wikidata.org/wiki/Q1187300","display_name":"Grammaticality","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C134714966","wikidata":"https://www.wikidata.org/wiki/Q6934448","display_name":"Multi-document summarization","level":3,"score":0.25220000743865967},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5121/csit.2026.160207","doi":"https://doi.org/10.5121/csit.2026.160207","is_oa":true,"landing_page_url":"https://doi.org/10.5121/csit.2026.160207","pdf_url":"https://doi.org/10.5121/csit.2026.160207","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Science and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5121/csit.2026.160207","doi":"https://doi.org/10.5121/csit.2026.160207","is_oa":true,"landing_page_url":"https://doi.org/10.5121/csit.2026.160207","pdf_url":"https://doi.org/10.5121/csit.2026.160207","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Science and Machine Learning","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7130656132.pdf","grobid_xml":"https://content.openalex.org/works/W7130656132.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4416117090","https://openalex.org/W4412036405","https://openalex.org/W4416030368","https://openalex.org/W4387725294","https://openalex.org/W4393028467","https://openalex.org/W4402594069","https://openalex.org/W4410838662","https://openalex.org/W4413779719","https://openalex.org/W7029247966","https://openalex.org/W4406660386"],"abstract_inverted_index":{"Text":[0],"summarization":[1,13],"is":[2],"crucial":[3],"for":[4],"mitigating":[5],"information":[6],"overload":[7],"across":[8,15],"domains.":[9],"This":[10],"research":[11],"evaluates":[12],"performance":[14],"17":[16],"large":[17],"language":[18],"models":[19,64,86],"using":[20],"seven":[21],"diverse":[22],"datasets":[23],"at":[24,106,113],"three":[25],"output":[26],"lengths":[27],"(50,":[28],"100,":[29],"150":[30,114],"tokens).":[31,115],"We":[32,97],"employ":[33],"a":[34,99],"novel":[35],"multi-dimensional":[36],"framework":[37],"assessing":[38],"factual":[39,67,103],"consistency,":[40],"semantic":[41],"similarity,":[42],"lexical":[43],"overlap,":[44],"and":[45,52,76,109],"human-like":[46,70],"evaluation":[47],"while":[48],"considering":[49],"both":[50],"quality":[51,71,111],"efficiency":[53,74],"factors.":[54],"Key":[55],"findings":[56],"reveal":[57],"significant":[58],"differences":[59],"between":[60,102],"models,":[61],"with":[62,85],"specific":[63],"excelling":[65],"in":[66],"accuracy":[68],"(deepseek-v3),":[69],"(claude-3-5-sonnet),":[72],"processing":[73],"(gemini-1.5-flash),":[75],"cost":[77],"effectiveness":[78],"(gemini-1.5-flash).":[79],"Performance":[80],"varies":[81],"dramatically":[82],"by":[83],"dataset,":[84],"struggling":[87],"on":[88,94],"technical":[89],"domains":[90],"but":[91],"performing":[92],"well":[93],"conversational":[95],"content.":[96],"identified":[98],"critical":[100],"tension":[101],"consistency":[104],"(best":[105,112],"50":[107],"tokens)":[108],"perceived":[110]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-20T00:00:00"}
