{"id":"https://openalex.org/W4417029943","doi":"https://doi.org/10.18653/v1/2026.findings-acl.859","title":"Understanding LLM Reasoning for Abstractive Summarization","display_name":"Understanding LLM Reasoning for Abstractive Summarization","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4417029943","doi":"https://doi.org/10.18653/v1/2026.findings-acl.859"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.859","doi":"https://doi.org/10.18653/v1/2026.findings-acl.859","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.859","pdf_url":"https://aclanthology.org/2026.findings-acl.859.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2026.findings-acl.859.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002795429","display_name":"Haohan Yuan","orcid":"https://orcid.org/0009-0001-1204-3484","observed_orcids":["https://orcid.org/0009-0001-1204-3484","https://orcid.org/0009-0004-8955-1761"]},"institutions":[{"id":"https://openalex.org/I117965899","display_name":"University of Hawai\u02bbi at M\u0101noa","ror":"https://ror.org/01wspgy28","country_code":"US","type":"education","lineage":["https://openalex.org/I117965899"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haohan Yuan","raw_affiliation_strings":["ALOHA Lab , University of Hawaii at Manoa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ALOHA Lab , University of Hawaii at Manoa","institution_ids":["https://openalex.org/I117965899"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100729435","display_name":"Haopeng Zhang","orcid":"https://orcid.org/0000-0003-1981-8307","observed_orcids":["https://orcid.org/0000-0003-1981-8307"]},"institutions":[{"id":"https://openalex.org/I117965899","display_name":"University of Hawai\u02bbi at M\u0101noa","ror":"https://ror.org/01wspgy28","country_code":"US","type":"education","lineage":["https://openalex.org/I117965899"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haopeng Zhang","raw_affiliation_strings":["ALOHA Lab , University of Hawaii at Manoa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ALOHA Lab , University of Hawaii at Manoa","institution_ids":["https://openalex.org/I117965899"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I117965899"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02135703,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"17360","last_page":"17386"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9628999829292297,"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":0.9628999829292297,"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":0.015799999237060547,"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":0.006800000090152025,"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/abstractive-summarization","display_name":"abstractive summarization","score":0.8690000176429749},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.8619999885559082},{"id":"https://openalex.org/keywords/reasoning-strategies","display_name":"reasoning strategies","score":0.7900000214576721},{"id":"https://openalex.org/keywords/large-reasoning-models","display_name":"large reasoning models","score":0.9480000138282776},{"id":"https://openalex.org/keywords/summary-quality","display_name":"summary quality","score":0.7480000257492065},{"id":"https://openalex.org/keywords/explicit-reasoning","display_name":"explicit reasoning","score":0.550000011920929},{"id":"https://openalex.org/keywords/implicit-reasoning","display_name":"implicit reasoning","score":0.9990000128746033},{"id":"https://openalex.org/keywords/factual-consistency","display_name":"factual consistency","score":0.6330000162124634}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.8392999768257141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7113999724388123},{"id":"https://openalex.org/C2777413886","wikidata":"https://www.wikidata.org/wiki/Q3276013","display_name":"Fluency","level":2,"score":0.6686000227928162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5378000140190125},{"id":"https://openalex.org/C86827895","wikidata":"https://www.wikidata.org/wiki/Q7098582","display_name":"Opportunistic reasoning","level":4,"score":0.5198000073432922},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4957999885082245},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.4787999987602234},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C36964233","wikidata":"https://www.wikidata.org/wiki/Q7920942","display_name":"Verbal reasoning","level":3,"score":0.4101000130176544},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3788999915122986},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.3653999865055084},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.35530000925064087},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C103057564","wikidata":"https://www.wikidata.org/wiki/Q4751139","display_name":"Analytic reasoning","level":3,"score":0.33629998564720154},{"id":"https://openalex.org/C2779525943","wikidata":"https://www.wikidata.org/wiki/Q1187300","display_name":"Grammaticality","level":3,"score":0.33489999175071716},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.295199990272522},{"id":"https://openalex.org/C166088908","wikidata":"https://www.wikidata.org/wiki/Q308495","display_name":"Abductive reasoning","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.859","doi":"https://doi.org/10.18653/v1/2026.findings-acl.859","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.859","pdf_url":"https://aclanthology.org/2026.findings-acl.859.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2512.03503","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2512.03503","pdf_url":"https://arxiv.org/pdf/2512.03503","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.03503","doi":"https://doi.org/10.48550/arxiv.2512.03503","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.03503","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.859","doi":"https://doi.org/10.18653/v1/2026.findings-acl.859","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.859","pdf_url":"https://aclanthology.org/2026.findings-acl.859.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2255260543","display_name":"SCH: Personalized Machine Learning for Repeat Adverse Health Events using Novel Multimodal Self-Supervised Pretraining Methods","funder_award_id":"2406251","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2819759025","display_name":"Collaborative Research: CSSI Frameworks: SAGE3: Smart Amplified Group Environment for Harnessing the Data Revolution","funder_award_id":"2004014","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4006316252","display_name":"Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS)","funder_award_id":"2138259","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7065548098","display_name":"RII Track-1: Change Hawaii: Harnessing the Data Revolution for Island Resilience","funder_award_id":"2149133","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/W4417029943.pdf","grobid_xml":"https://content.openalex.org/works/W4417029943.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4416033588","https://openalex.org/W4414521686","https://openalex.org/W4409362655","https://openalex.org/W4389520387","https://openalex.org/W4391092921","https://openalex.org/W4411113139","https://openalex.org/W4389520163","https://openalex.org/W7164025300","https://openalex.org/W7090834724","https://openalex.org/W4415795390"],"abstract_inverted_index":{"Reasoning":[0,50],"has":[1],"substantially":[2],"improved":[3],"Large":[4,49],"Language":[5],"Models":[6,51],"(LLMs)":[7],"on":[8,78],"analytical":[9],"tasks":[10],"such":[11],"as":[12],"mathematics":[13],"and":[14,37,47,61,73,81,93,130],"code":[15,157],"generation,":[16],"but":[17,102],"its":[18,74],"value":[19],"for":[20,139],"abstractive":[21],"summarization":[22,35,83,129],"remains":[23],"unclear.To":[24],"address":[25],"this":[26],"gap,":[27],"we":[28,86],"adapt":[29],"general":[30],"reasoning":[31,45,67,96,109,123,142,147],"strategies":[32,46,97],"to":[33],"the":[34,79,82,113],"setting":[36],"conduct":[38],"a":[39,70,88],"large-scale":[40],"comparative":[41],"study":[42],"of":[43],"8":[44,54],"3":[48],"(LRMs)":[52],"across":[53],"diverse":[55],"datasets,":[56],"evaluating":[57],"both":[58],"summary":[59,91],"quality":[60,92],"factual":[62,94,105,134],"faithfulness.Our":[63],"results":[64],"show":[65],"that":[66,118],"is":[68,143,158],"not":[69,126,144],"universal":[71],"solution":[72],"effectiveness":[75],"depends":[76],"strongly":[77],"strategy":[80],"setting.In":[84],"particular,":[85],"find":[87,117],"trade-off":[89],"between":[90],"faithfulness.Explicit":[95],"often":[98],"improve":[99,128],"reference-based":[100],"quality,":[101],"may":[103],"weaken":[104],"grounding,":[106],"whereas":[107],"implicit":[108],"in":[110],"LRMs":[111],"shows":[112],"opposite":[114],"tendency.We":[115],"further":[116],"increasing":[119],"an":[120],"LRM's":[121],"internal":[122],"budget":[124],"does":[125],"reliably":[127],"can":[131],"even":[132],"reduce":[133],"consistency.These":[135],"findings":[136],"suggest":[137],"that,":[138],"summarization,":[140],"more":[141],"always":[145],"better.Effective":[146],"should":[148],"preserve":[149],"faithful":[150],"compression":[151],"rather":[152],"than":[153],"induce":[154],"over-elaboration.Our":[155],"source":[156],"publicly":[159],"available.":[160]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2025-12-05T00:00:00"}
