{"id":"https://openalex.org/W7152434896","doi":"https://doi.org/10.48550/arxiv.2604.06416","title":"Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries","display_name":"Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7152434896","doi":"https://doi.org/10.48550/arxiv.2604.06416"},"language":"en","primary_location":{"id":"pmh:oai:pure.atira.dk:publications/1911981e-7da5-4896-bab6-043c640110fd","is_oa":false,"landing_page_url":"https://pure.au.dk/portal/en/publications/1911981e-7da5-4896-bab6-043c640110fd","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Hicke, R M M, Hamilton, S, Mimno, D & Kristensen-McLachlan, R D 2026 'Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries'. https://doi.org/10.48550/arXiv.2604.06416","raw_type":"info:eu-repo/semantics/preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.06416","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133270620","display_name":"Rebecca M. M. Hicke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hicke, Rebecca M. M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057134447","display_name":"Sil Hamilton","orcid":"https://orcid.org/0000-0002-6579-4628"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamilton, Sil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086934220","display_name":"David Mimno","orcid":"https://orcid.org/0000-0001-7510-9404"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mimno, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133080543","display_name":"Ross Deans Kristensen-McLachlan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kristensen-McLachlan, Ross Deans","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8277999758720398,"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":0.8277999758720398,"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/T13629","display_name":"Text Readability and Simplification","score":0.044599998742341995,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.03020000085234642,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/narrative","display_name":"Narrative","score":0.7452999949455261},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.6453999876976013},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6288999915122986},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.6244000196456909},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5709999799728394},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.47699999809265137}],"concepts":[{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.7452999949455261},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.6453999876976013},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.6244000196456909},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5709999799728394},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5461999773979187},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C14224292","wikidata":"https://www.wikidata.org/wiki/Q13600188","display_name":"Conceptual framework","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.365200012922287},{"id":"https://openalex.org/C13606891","wikidata":"https://www.wikidata.org/wiki/Q2623243","display_name":"Conceptual model","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3456000089645386},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31630000472068787},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C2776538412","wikidata":"https://www.wikidata.org/wiki/Q989963","display_name":"Storytelling","level":3,"score":0.2766999900341034},{"id":"https://openalex.org/C2778143727","wikidata":"https://www.wikidata.org/wiki/Q1820650","display_name":"Readability","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pure.atira.dk:publications/1911981e-7da5-4896-bab6-043c640110fd","is_oa":false,"landing_page_url":"https://pure.au.dk/portal/en/publications/1911981e-7da5-4896-bab6-043c640110fd","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Hicke, R M M, Hamilton, S, Mimno, D & Kristensen-McLachlan, R D 2026 'Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries'. https://doi.org/10.48550/arXiv.2604.06416","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2604.06416","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06416","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.06416","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06416","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.46055182814598083}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Although":[0],"LLM":[1],"context":[2],"lengths":[3],"have":[4],"grown,":[5],"there":[6],"is":[7],"evidence":[8],"that":[9],"their":[10,142],"ability":[11],"to":[12,177],"integrate":[13],"information":[14],"across":[15],"long-form":[16],"texts":[17,133],"has":[18],"not":[19],"kept":[20],"pace.":[21],"We":[22,85,102,173],"evaluate":[23],"one":[24],"such":[25],"understanding":[26],"task:":[27],"generating":[28],"summaries":[29,36,78,108],"of":[30,35,62,89,97,115,152],"novels.":[31],"When":[32],"human":[33,50,60,122,155],"authors":[34],"compress":[37],"a":[38,100,145],"story,":[39],"they":[40,43,83],"reveal":[41],"what":[42],"consider":[44],"narratively":[45],"important.":[46],"Therefore,":[47],"by":[48,109],"comparing":[49],"and":[51,105,123,134,139,168],"LLM-authored":[52],"summaries,":[53,125],"we":[54,71,126],"can":[55],"assess":[56],"whether":[57],"models":[58,148],"mirror":[59],"patterns":[61],"conceptual":[63,69],"engagement":[64,157],"with":[65,79,147,158],"texts.":[66,119,153],"To":[67],"measure":[68],"engagement,":[70],"align":[72,106],"sentences":[73],"from":[74],"150":[75,117],"human-written":[76],"novel":[77],"the":[80,87,95,116,121,132,150],"specific":[81],"chapters":[82],"reference.":[84],"demonstrate":[86],"difficulty":[88],"this":[90],"alignment":[91],"task,":[92],"which":[93],"indicates":[94],"complexity":[96],"summarization":[98],"as":[99],"task.":[101],"then":[103],"generate":[104],"additional":[107],"nine":[110],"state-of-the-art":[111],"LLMs":[112,140],"for":[113,164,170],"each":[114],"reference":[118],"Comparing":[120,154],"model-authored":[124],"find":[127],"both":[128],"stylistic":[129],"differences":[130,135],"between":[131],"in":[136],"how":[137],"humans":[138],"distribute":[141],"focus":[143],"throughout":[144],"narrative,":[146],"emphasizing":[149],"ends":[151],"narrative":[156,166],"model":[159],"attention":[160],"mechanisms":[161],"suggests":[162],"explanations":[163],"degraded":[165],"comprehension":[167],"targets":[169],"future":[171,179],"development.":[172],"release":[174],"our":[175],"dataset":[176],"support":[178],"research.":[180]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-10T00:00:00"}