{"id":"W2141694739","doi":"10.1145/544220.544227","title":"Using librarian techniques in automatic text summarization for information retrieval","year":2002,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; National Science Foundation","keywords":"Automatic summarization; Computer science; Information retrieval; Multi-document summarization; Operationalization; World Wide Web; Seekers","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005925058,0.001608325,0.001274033,0.00933485,0.001388045,0.003239212,0.001583857,0.0009806154,0.004429194],"category_scores_gemma":[0.01698956,0.0007849557,0.001302055,0.007794622,0.0008452297,0.00488404,0.001641509,0.001490714,0.004608165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007352977,"about_ca_system_score_gemma":0.001105263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002385454,"about_ca_topic_score_gemma":0.004637374,"domain_scores_codex":[0.9949747,0.002675042,0.0005199079,0.0008083279,0.0008861526,0.0001359554],"domain_scores_gemma":[0.9898947,0.006047712,0.0009540849,0.001113929,0.001872063,0.0001175899],"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":[0.0001663533,0.0001674992,0.002570592,0.0008076348,0.0002124613,0.0001180459,0.002860034,0.008778193,0.02303594,0.009179954,0.00932061,0.9427827],"study_design_scores_gemma":[0.0003557542,0.001616947,0.01876175,0.0005725372,0.001516313,0.001641258,0.004180358,0.4751219,0.1774752,0.08273021,0.2353119,0.0007159044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00853607,0.001233,0.9808896,0.0003848515,0.00004940771,0.0002695748,0.0002825329,0.006327945,0.002027103],"genre_scores_gemma":[0.05967515,0.001140089,0.9327757,0.0001930028,0.0002158756,0.0004203314,0.001656477,0.0006925773,0.003230795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00933485,"threshold_uncertainty_score":0.03133506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04868112608135101,"score_gpt":0.2590906807895311,"score_spread":0.2104095547081801,"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."}}