{"id":"https://openalex.org/W4284688083","doi":"https://doi.org/10.1145/3477495.3531906","title":"MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization","display_name":"MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4284688083","doi":"https://doi.org/10.1145/3477495.3531906"},"language":"en","primary_location":{"id":"doi:10.1145/3477495.3531906","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531906","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5031898245","display_name":"Qianren Mao","orcid":"https://orcid.org/0000-0003-0780-0628"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qianren Mao","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110380542","display_name":"Hongdong Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongdong Zhu","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010328805","display_name":"Junnan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junnan Liu","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085952082","display_name":"Cheng Ji","orcid":"https://orcid.org/0000-0003-2513-3822"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Ji","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100740622","display_name":"Hao Peng","orcid":"https://orcid.org/0000-0003-0458-5977"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Peng","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100380463","display_name":"Jianxin Li","orcid":"https://orcid.org/0000-0001-5152-0055"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxin Li","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443578","display_name":"Lihong Wang","orcid":"https://orcid.org/0000-0003-0179-2364"},"institutions":[{"id":"https://openalex.org/I4210087772","display_name":"National Computer Network Emergency Response Technical Team/Coordination Center of Chinar","ror":"https://ror.org/00247dh76","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210087772"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihong Wang","raw_affiliation_strings":["CNCERT, Beijing, China"],"affiliations":[{"raw_affiliation_string":"CNCERT, Beijing, China","institution_ids":["https://openalex.org/I4210087772"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100401045","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0001-6157-0662"},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["University of Leeds, Leeds, West Yorkshire, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Leeds, Leeds, West Yorkshire, United Kingdom","institution_ids":["https://openalex.org/I130828816"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5031898245"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.5197,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.62039431,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2617","last_page":"2622"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9998000264167786,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9848999977111816,"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":0.9046683311462402},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8540185689926147},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6744416952133179},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6040960550308228},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5520930886268616},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5318557620048523},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.5015380382537842},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4868432879447937},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44337964057922363},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3603646159172058}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9046683311462402},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8540185689926147},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6744416952133179},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6040960550308228},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5520930886268616},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5318557620048523},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.5015380382537842},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4868432879447937},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44337964057922363},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3603646159172058},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3477495.3531906","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531906","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:185779","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Proceedings Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5199999809265137,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G4682916240","display_name":null,"funder_award_id":"U20B2053","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1525595230","https://openalex.org/W2101267652","https://openalex.org/W2110693578","https://openalex.org/W2187089797","https://openalex.org/W2896457183","https://openalex.org/W2896807716","https://openalex.org/W2950342809","https://openalex.org/W2952138241","https://openalex.org/W2962785754","https://openalex.org/W2962972512","https://openalex.org/W2964028111","https://openalex.org/W2970419734","https://openalex.org/W3034353423","https://openalex.org/W3034961030","https://openalex.org/W3035050380","https://openalex.org/W3039075121","https://openalex.org/W3100053428","https://openalex.org/W3100993589","https://openalex.org/W3105816068","https://openalex.org/W3198534342","https://openalex.org/W3211389291","https://openalex.org/W6600708310","https://openalex.org/W6602452458","https://openalex.org/W6713523276"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W1517524280","https://openalex.org/W4389760904","https://openalex.org/W4306886878","https://openalex.org/W2275988210"],"abstract_inverted_index":{"Recent":[0],"studies":[1],"of":[2,58,151],"extractive":[3,86,157,190],"text":[4,29,87],"summarization":[5,30,174,191],"have":[6],"leveraged":[7],"BERT":[8],"for":[9,24,28,40,85,156,172],"document":[10],"encoding":[11],"with":[12,186],"breakthrough":[13],"performance.":[14],"However,":[15],"when":[16],"using":[17],"a":[18,82,91,131],"pre-trained":[19],"BERT-based":[20],"encoder,":[21],"existing":[22],"approaches":[23],"selecting":[25],"representative":[26],"sentences":[27,47,60],"are":[31,154],"inadequate":[32],"since":[33],"the":[34,45,59,113,139,148],"encoder":[35],"is":[36,61,90,135],"not":[37,62],"explicitly":[38,98],"trained":[39],"representing":[41],"sentences.":[42],"Simply":[43],"providing":[44],"BERT-initialized":[46,188],"to":[48,54,68,97,111,137,168],"cross-sentential":[49],"graph-based":[50,173,189],"neural":[51],"networks":[52],"(GNNs)":[53],"encode":[55,112],"semantic":[56],"features":[57,72,171],"ideal":[63],"because":[64],"doing":[65],"so":[66],"fail":[67],"integrate":[69],"other":[70],"summary-worthy":[71,102],"like":[73],"sentence":[74,149],"importance":[75],"and":[76,120],"positions.":[77],"This":[78],"paper":[79],"presents":[80],"MuchSUM,":[81],"better":[83],"approach":[84],"summarization.":[88,158],"MuchSUM":[89],"multi-channel":[92],"graph":[93,109,141],"convolutional":[94],"network":[95],"designed":[96,136],"incorporate":[99],"multiple":[100],"salient":[101],"features.":[103],"Specifically,":[104],"we":[105],"introduce":[106],"three":[107,162],"specific":[108],"channels":[110],"node":[114,117,121],"textual":[115],"features,":[116,119,123],"centrality":[118],"position":[122],"respectively,":[124],"under":[125],"bipartite":[126],"word-sentence":[127],"heterogeneous":[128],"graphs.":[129],"Then,":[130],"cross-channel":[132],"convolution":[133],"operation":[134],"distill":[138],"common":[140],"representations":[142,150],"shared":[143],"by":[144],"different":[145],"channels.":[146],"Finally,":[147],"each":[152,166],"channel":[153,167],"fused":[155],"We":[159],"also":[160],"investigate":[161],"weighted":[163],"graphs":[164],"in":[165],"infuse":[169],"edge":[170],"modeling.":[175],"Experimental":[176],"results":[177],"demonstrate":[178],"our":[179],"model":[180],"can":[181],"achieve":[182],"considerable":[183],"performance":[184],"compared":[185],"some":[187],"systems.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}