{"id":"https://openalex.org/W3156544353","doi":"https://doi.org/10.1145/3404835.3463111","title":"Unsupervised Extractive Text Summarization with Distance-Augmented Sentence Graphs","display_name":"Unsupervised Extractive Text Summarization with Distance-Augmented Sentence Graphs","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3156544353","doi":"https://doi.org/10.1145/3404835.3463111","mag":"3156544353"},"language":"en","primary_location":{"id":"doi:10.1145/3404835.3463111","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3404835.3463111","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3404835.3463111","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3404835.3463111","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101585691","display_name":"Jingzhou Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingzhou Liu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102159537","display_name":"Dominic J. D. Hughes","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dominic J. D. Hughes","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106542734","display_name":"Yiming Yang","orcid":"https://orcid.org/0009-0006-3569-0023"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiming Yang","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3702,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.93878733,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2313","last_page":"2317"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","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/T13083","display_name":"Advanced Text Analysis Techniques","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/T10028","display_name":"Topic Modeling","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"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9976999759674072,"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":0.9632582664489746},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8488661050796509},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6333178877830505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6113604307174683},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5676253437995911},{"id":"https://openalex.org/keywords/multi-document-summarization","display_name":"Multi-document summarization","score":0.5472403168678284},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5468742847442627},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5047820806503296},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44559717178344727},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.44217729568481445},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.06500282883644104}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9632582664489746},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8488661050796509},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6333178877830505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6113604307174683},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5676253437995911},{"id":"https://openalex.org/C134714966","wikidata":"https://www.wikidata.org/wiki/Q6934448","display_name":"Multi-document summarization","level":3,"score":0.5472403168678284},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5468742847442627},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5047820806503296},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44559717178344727},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.44217729568481445},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.06500282883644104},{"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:10.1145/3404835.3463111","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3404835.3463111","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3404835.3463111","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3404835.3463111","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3404835.3463111","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3404835.3463111","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G2581707695","display_name":"BIGDATA: F: Large-Scale Transductive Learning from Heterogeneous Data Sources","funder_award_id":"1546329","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5420615920","display_name":null,"funder_award_id":"IIS-1546329","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3156544353.pdf","grobid_xml":"https://content.openalex.org/works/W3156544353.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W1525595230","https://openalex.org/W1603598191","https://openalex.org/W1854214752","https://openalex.org/W2083305840","https://openalex.org/W2110693578","https://openalex.org/W2896807716","https://openalex.org/W2949615363","https://openalex.org/W2962985882","https://openalex.org/W3034715004","https://openalex.org/W3042942426","https://openalex.org/W3101913037","https://openalex.org/W3105230369","https://openalex.org/W3138773240","https://openalex.org/W3158986179","https://openalex.org/W6603059720","https://openalex.org/W6795224213"],"related_works":["https://openalex.org/W3164984162","https://openalex.org/W2104677027","https://openalex.org/W2902627734","https://openalex.org/W2112885393","https://openalex.org/W2065541085","https://openalex.org/W2785821657","https://openalex.org/W2173208124","https://openalex.org/W2568827738","https://openalex.org/W1990695371","https://openalex.org/W2365100044"],"abstract_inverted_index":{"Supervised":[0],"summarization":[1,69,91,123],"has":[2],"made":[3],"significant":[4],"improvements":[5],"in":[6,42,78,125],"recent":[7],"years":[8],"by":[9,96],"leveraging":[10],"cutting-edge":[11],"deep":[12],"learning":[13],"technologies.":[14],"However,":[15],"the":[16,24,73,79,110,132],"true":[17],"success":[18],"of":[19,26,29,32,81],"supervised":[20,138],"methods":[21,124],"relies":[22],"on":[23,71,107],"availability":[25],"large":[27],"quantity":[28],"human-generated":[30],"summaries":[31],"documents,":[33],"which":[34,54],"is":[35,134],"highly":[36],"costly":[37],"and":[38,75,128,131],"difficult":[39],"to":[40,50,64,92,136],"obtain":[41],"general.":[43],"This":[44],"paper":[45],"proposes":[46],"an":[47,56],"unsupervised":[48,121],"approach":[49,88,112],"extractive":[51,122],"text":[52],"summarization,":[53],"uses":[55],"automatically":[57],"constructed":[58],"sentence":[59],"graph":[60],"from":[61,89],"each":[62,82],"document":[63],"select":[65],"salient":[66],"sentences":[67],"for":[68],"based":[70],"both":[72,126],"similarities":[74],"relative":[76],"distances":[77],"neighborhood":[80],"sentences.":[83],"We":[84],"further":[85],"generalize":[86],"our":[87,105],"single-document":[90,127],"a":[93],"multi-document":[94,129],"setting,":[95],"aggregating":[97],"document-level":[98],"graphs":[99],"via":[100],"proximity-based":[101],"cross-document":[102],"edges.":[103],"In":[104],"experiments":[106],"benchmark":[108],"datasets,":[109],"proposed":[111],"achieved":[113],"competitive":[114,135],"or":[115],"better":[116],"results":[117],"than":[118],"previous":[119],"state-of-the-art":[120],"settings,":[130],"performance":[133],"strong":[137],"baselines.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}