{"id":"https://openalex.org/W7160997052","doi":"https://doi.org/10.48550/arxiv.2605.11753","title":"Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention","display_name":"Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7160997052","doi":"https://doi.org/10.48550/arxiv.2605.11753"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11753","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11753","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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.11753","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136032832","display_name":"Abid Ali","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ali, Abid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136034870","display_name":"Diego Molla-Aliod","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Molla-Aliod, Diego","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136055204","display_name":"Usman Naseem","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naseem, Usman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5136032832"],"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9818000197410583,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9818000197410583,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.004000000189989805,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.0031999999191612005,"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":0.8906999826431274},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6157000064849854},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5564000010490417},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5331000089645386},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.42899999022483826},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4034999907016754},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.3928000032901764},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3617999851703644}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.8906999826431274},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7964000105857849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6592000126838684},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6157000064849854},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5564000010490417},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5331000089645386},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.489300012588501},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.42899999022483826},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30790001153945923},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C2776085556","wikidata":"https://www.wikidata.org/wiki/Q183361","display_name":"Chen","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2802000045776367},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26429998874664307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11753","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11753","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.2605.11753","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11753","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7709197998046875}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"summarization":[1,45],"requires":[2],"models":[3],"to":[4,11,29],"jointly":[5,42],"understand":[6],"textual":[7],"and":[8,32,46,58,101,128,142,153],"visual":[9,22,75],"inputs":[10],"generate":[12],"concise,":[13],"semantically":[14],"coherent":[15],"summaries.":[16],"Existing":[17],"methods":[18],"often":[19],"inject":[20],"shallow":[21],"features":[23],"into":[24],"deep":[25],"language":[26,79],"models,":[27],"leading":[28],"representational":[30],"mismatches":[31],"weak":[33],"cross-modal":[34,126],"grounding.":[35],"We":[36],"propose":[37],"a":[38,68,93,109,119],"unified":[39],"framework":[40],"that":[41,88,122,133],"performs":[43],"text":[44],"representative":[47,145],"image":[48,155],"selection.":[49],"Our":[50],"system,":[51],"SPeCTrA-Sum":[52,115],"(Sampler":[53],"Perceiver":[54],"with":[55,77],"Cross-modal":[56],"Transformer":[57],"gated":[59],"Attention":[60],"for":[61,157],"Summarization),":[62],"introduces":[63],"two":[64],"key":[65],"innovations.":[66],"First,":[67],"Deep":[69],"Visual":[70,95],"Processor":[71],"(DVP)":[72],"aligns":[73],"the":[74,78,148],"encoder":[76],"model":[80],"at":[81],"corresponding":[82],"depths,":[83],"enabling":[84],"hierarchical,":[85],"layer-wise":[86],"fusion":[87,152],"preserves":[89],"semantic":[90],"consistency.":[91],"Second,":[92],"lightweight":[94],"Relevance":[96],"Predictor":[97],"(VRP)":[98],"selects":[99,143],"salient":[100],"diverse":[102],"images":[103],"by":[104],"distilling":[105],"soft":[106],"labels":[107],"from":[108],"Determinantal":[110],"Point":[111],"Processes":[112],"(DPP)":[113],"teacher.":[114],"is":[116],"trained":[117],"using":[118],"multi-objective":[120],"loss":[121],"combines":[123],"autoregressive":[124],"summarization,":[125],"alignment,":[127],"DPP-based":[129],"distillation.":[130],"Experiments":[131],"show":[132],"our":[134],"system":[135],"produces":[136],"more":[137,144],"accurate,":[138],"visually":[139],"grounded":[140],"summaries":[141],"images,":[146],"demonstrating":[147],"benefits":[149],"of":[150],"depth-aware":[151],"principled":[154],"selection":[156],"multimodal":[158],"summarization.":[159]},"counts_by_year":[],"updated_date":"2026-05-14T06:16:12.342656","created_date":"2026-05-14T00:00:00"}
