{"id":"https://openalex.org/W4405633574","doi":"https://doi.org/10.1109/mlnlp63328.2024.10800044","title":"Fact-Aware Abstractive Summarization Based on Prompt Information and Re-Ranking","display_name":"Fact-Aware Abstractive Summarization Based on Prompt Information and Re-Ranking","publication_year":2024,"publication_date":"2024-10-18","ids":{"openalex":"https://openalex.org/W4405633574","doi":"https://doi.org/10.1109/mlnlp63328.2024.10800044"},"language":"en","primary_location":{"id":"doi:10.1109/mlnlp63328.2024.10800044","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlnlp63328.2024.10800044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)","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/A5085227576","display_name":"Tianyu Cai","orcid":"https://orcid.org/0009-0009-9599-7248"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyu Cai","raw_affiliation_strings":["Sichuan University,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University,Chengdu,China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026464235","display_name":"Yurui Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yurui Yang","raw_affiliation_strings":["Sichuan University,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University,Chengdu,China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038107623","display_name":"Yujie Wan","orcid":"https://orcid.org/0000-0003-3121-3386"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujie Wan","raw_affiliation_strings":["Sichuan University,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University,Chengdu,China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060942172","display_name":"Xingjing Mao","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingjing Mao","raw_affiliation_strings":["Sichuan University,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University,Chengdu,China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042930315","display_name":"Shenggen Ju","orcid":"https://orcid.org/0000-0003-3730-0755"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenggen Ju","raw_affiliation_strings":["Sichuan University,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University,Chengdu,China","institution_ids":["https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3055,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67770364,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9945999979972839,"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":0.9945999979972839,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9848999977111816,"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":0.9186991453170776},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7915743589401245},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5861062407493591},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5307011008262634},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.47421836853027344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3991646468639374}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9186991453170776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7915743589401245},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5861062407493591},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5307011008262634},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47421836853027344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3991646468639374}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlnlp63328.2024.10800044","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlnlp63328.2024.10800044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2697430830","display_name":null,"funder_award_id":"2023YFG0265","funder_id":"https://openalex.org/F4320336596","funder_display_name":"Key Research and Development Program of Sichuan Province"},{"id":"https://openalex.org/G4466500611","display_name":null,"funder_award_id":"62137001","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"},{"id":"https://openalex.org/F4320336596","display_name":"Key Research and Development Program of Sichuan Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2101105183","https://openalex.org/W2768957049","https://openalex.org/W2889518897","https://openalex.org/W2896457183","https://openalex.org/W2951211142","https://openalex.org/W2962965405","https://openalex.org/W2964285114","https://openalex.org/W3100439847","https://openalex.org/W3102489149","https://openalex.org/W3106234277","https://openalex.org/W3141940864","https://openalex.org/W3153193605","https://openalex.org/W3169283369","https://openalex.org/W3173210704","https://openalex.org/W3174073137","https://openalex.org/W3190367510","https://openalex.org/W3199926081","https://openalex.org/W4205480693","https://openalex.org/W4225934689","https://openalex.org/W4280625699","https://openalex.org/W4385245566","https://openalex.org/W4385734149","https://openalex.org/W6682631176","https://openalex.org/W6753207981","https://openalex.org/W6762122294","https://openalex.org/W6768021236","https://openalex.org/W6769627184","https://openalex.org/W6771915120","https://openalex.org/W6843992823"],"related_works":["https://openalex.org/W402673672","https://openalex.org/W2118564381","https://openalex.org/W2163901716","https://openalex.org/W2152204162","https://openalex.org/W2739821120","https://openalex.org/W2088097596","https://openalex.org/W2150136235","https://openalex.org/W2140661912","https://openalex.org/W2037724912","https://openalex.org/W2056806613"],"abstract_inverted_index":{"Abstractive":[0],"text":[1,92],"summarization":[2],"helps":[3],"people":[4],"quickly":[5],"obtain":[6,94],"the":[7,40,48,64,84,90,102,114,130,141,156,171,176,191,194],"key":[8,27,75],"information":[9,61,100],"of":[10,42,69,158,182,193],"an":[11],"article,":[12],"and":[13,59,66,74,80,111,117,122,136,166,184,187],"existing":[14],"models":[15],"generate":[16],"fluent":[17],"summaries":[18,43,154],"but":[19],"often":[20],"suffer":[21],"from":[22,101],"factual":[23,67,99,112,185],"consistency":[24,68],"problems,":[25],"a":[26,51,123],"issue":[28],"that":[29,56,97,170],"current":[30],"research":[31],"has":[32],"not":[33],"adequately":[34],"addressed.":[35],"In":[36,105,139],"order":[37,106],"to":[38,62,93,107,128,146,151],"ensure":[39],"quality":[41,65,110,183],"while":[44],"improving":[45],"their":[46],"factualness,":[47],"article":[49],"proposes":[50],"factaware":[52],"summary":[53],"generation":[54],"method":[55,174],"combines":[57],"reordering":[58,124],"prompting":[60],"improve":[63],"generated":[70],"summaries.":[71,138],"Keyword":[72],"extraction":[73],"phrases":[76],"are":[77,120],"first":[78],"introduced":[79],"then":[81],"fed":[82],"into":[83],"generative":[85],"abstract":[86,109],"model":[87,131,142,179],"along":[88],"with":[89],"original":[91,103],"candidate":[95],"abstracts":[96],"incorporate":[98],"text.":[104],"combine":[108],"consistency,":[113,186],"ROUGE":[115],"metrics":[116,119],"FactCC":[118],"combined,":[121],"rule":[125],"is":[126,143],"designed":[127],"guide":[129],"in":[132,180],"generating":[133],"more":[134,152],"realistic":[135],"content-rich":[137],"addition,":[140],"further":[144],"incentivized":[145],"assign":[147],"higher":[148],"probability":[149],"scores":[150],"authentic":[153],"through":[155],"introduction":[157],"contrast":[159],"loss.":[160],"Experimental":[161],"results":[162],"on":[163],"CNN/Daily":[164],"Mail":[165],"XSum":[167],"datasets":[168],"show":[169],"article's":[172],"proposed":[173,195],"outperforms":[175],"strong":[177],"baseline":[178],"terms":[181],"ablation":[188],"experiments":[189],"validate":[190],"effectiveness":[192],"module.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}