{"id":"W4402702960","doi":"10.1109/cvpr52733.2024.00796","title":"Scaling Up Video Summarization Pretraining with Large Language Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Automatic summarization; Computer science; Scaling; Natural language processing; Artificial intelligence; Language model; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001042426,0.002343082,0.001201016,0.001448136,0.0005993305,0.00101817,0.001772979,0.001276247,0.004070977],"category_scores_gemma":[0.005236971,0.0004750195,0.00126161,0.001332665,0.0003559371,0.00232377,0.0009817381,0.002579954,0.003773353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001061841,"about_ca_system_score_gemma":0.001363803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01184081,"about_ca_topic_score_gemma":0.02587409,"domain_scores_codex":[0.9991991,0.0001699674,0.00005794938,0.0003563545,0.0001278163,0.0000887979],"domain_scores_gemma":[0.9979675,0.001062049,0.0001310415,0.0002553598,0.0004908291,0.00009321047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004702133,0.0004844469,0.002096517,0.0006334168,0.000261361,0.0002827875,0.0002128533,0.08341379,0.04126223,0.001836908,0.05073391,0.8183117],"study_design_scores_gemma":[0.00006617892,0.0003004838,0.001328476,0.0000430313,0.0001011659,0.0001044738,0.0001807647,0.9665291,0.0194798,0.002883813,0.008947362,0.00003517938],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08590839,0.004272282,0.8489717,0.001562416,0.0007378994,0.0006445861,0.008873526,0.04400521,0.005024027],"genre_scores_gemma":[0.360664,0.001415876,0.5756883,0.001254945,0.0006045685,0.0009064455,0.0473131,0.001091406,0.01106132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01184081,"threshold_uncertainty_score":0.02354378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01230332878457298,"score_gpt":0.2431677259108387,"score_spread":0.2308643971262657,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}