{"id":"https://openalex.org/W3200575125","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533494","title":"SKGSUM: Abstractive Document Summarization with Semantic Knowledge Graphs","display_name":"SKGSUM: Abstractive Document Summarization with Semantic Knowledge Graphs","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200575125","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533494","mag":"3200575125"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533494","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533494","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5110768736","display_name":"Xin Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xin Ji","raw_affiliation_strings":["School of Software and Microelectronics Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software and Microelectronics Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101405285","display_name":"Wen Zhao","orcid":"https://orcid.org/0000-0002-5760-4759"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Zhao","raw_affiliation_strings":["School of Software and Microelectronics Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software and Microelectronics Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110768736"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":1.6316,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.86717494,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"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":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":0.9994999766349792,"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":0.9973000288009644,"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":0.905126690864563},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.885186493396759},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6584921479225159},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.6299833655357361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6118708252906799},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.5833959579467773},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5199683904647827},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.49247798323631287},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4566539525985718},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43590784072875977},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.09213408827781677}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.905126690864563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.885186493396759},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6584921479225159},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6299833655357361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6118708252906799},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.5833959579467773},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5199683904647827},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.49247798323631287},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4566539525985718},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43590784072875977},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.09213408827781677},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533494","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533494","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1544827683","https://openalex.org/W2110693578","https://openalex.org/W2123442489","https://openalex.org/W2250386865","https://openalex.org/W2250539671","https://openalex.org/W2251913848","https://openalex.org/W2606974598","https://openalex.org/W2788283780","https://openalex.org/W2888482885","https://openalex.org/W2889518897","https://openalex.org/W2896457183","https://openalex.org/W2899386490","https://openalex.org/W2949615363","https://openalex.org/W2950670227","https://openalex.org/W2952138241","https://openalex.org/W2952215948","https://openalex.org/W2962788840","https://openalex.org/W2962972512","https://openalex.org/W2963403868","https://openalex.org/W2963545005","https://openalex.org/W2964121744","https://openalex.org/W2964144561","https://openalex.org/W2970419734","https://openalex.org/W2971289520","https://openalex.org/W2971300525","https://openalex.org/W2998056485","https://openalex.org/W2998243528","https://openalex.org/W3018426321","https://openalex.org/W3035620455","https://openalex.org/W3035643691","https://openalex.org/W3100053428","https://openalex.org/W3101913037","https://openalex.org/W4297733535","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6632455782","https://openalex.org/W6691733968","https://openalex.org/W6731948947","https://openalex.org/W6739901393","https://openalex.org/W6755207826","https://openalex.org/W6755460622","https://openalex.org/W6776520508","https://openalex.org/W6785305436"],"related_works":["https://openalex.org/W2093597205","https://openalex.org/W2389846579","https://openalex.org/W2747680751","https://openalex.org/W2803026002","https://openalex.org/W2489740420","https://openalex.org/W2392495745","https://openalex.org/W132250100","https://openalex.org/W2472885054","https://openalex.org/W3014410397","https://openalex.org/W1539478205"],"abstract_inverted_index":{"In":[0,32],"abstractive":[1,55,66,131],"single-document":[2,67],"summarization":[3,56,132],"task,":[4],"generated":[5],"summaries":[6],"always":[7],"suffer":[8],"from":[9],"fabricated":[10],"and":[11,48,78,91,145,163],"less":[12],"informative":[13,162],"content.":[14],"An":[15],"intuitive":[16],"way":[17],"to":[18,23,100,116],"alleviate":[19],"this":[20,33],"problem":[21],"is":[22],"merge":[24],"external":[25],"semantic":[26,41],"knowledge":[27],"into":[28,53],"the":[29,58,83,97,102,107,114,129],"model":[30,64,138,159],"framework.":[31],"paper,":[34],"we":[35,112],"incorporate":[36],"explicit":[37,121],"graphs":[38],"based":[39,69],"on":[40,70,93,142],"knowledge,":[42,111],"including":[43],"term":[44],"frequency,":[45],"discourse":[46],"information,":[47],"entities":[49,79],"with":[50],"their":[51],"relations,":[52],"neural":[54,130],"for":[57,65,128],"problem.":[59],"We":[60],"propose":[61],"a":[62,125],"novel":[63],"Summarization":[68],"Semantic":[71],"Knowledge":[72],"Graphs":[73],"(SKGSUM),":[74],"which":[75],"regards":[76],"sentences":[77],"as":[80],"nodes,":[81],"captures":[82],"relations":[84],"between":[85],"units":[86],"in":[87,96,124],"different":[88,118],"textual":[89],"levels,":[90],"focuses":[92],"salient":[94],"content":[95],"source":[98],"documents":[99],"guide":[101],"summary":[103],"generation":[104],"process.":[105],"To":[106],"best":[108],"of":[109],"our":[110,137,158],"are":[113],"first":[115],"exploit":[117],"textual-unit":[119],"levels":[120],"graph":[122],"representations":[123],"unified":[126],"framework":[127],"task.":[133],"Results":[134],"show":[135],"that":[136,157],"achieves":[139],"significant":[140],"improvements":[141],"both":[143],"XSum":[144],"CNN/Daily":[146],"Mail":[147],"datasets":[148],"over":[149],"some":[150],"strong":[151],"baselines.":[152],"Human":[153],"evaluations":[154],"further":[155],"indicate":[156],"can":[160],"generate":[161],"coherent":[164],"summaries.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
