{"id":"W2033839684","doi":"10.3115/1613715.1613813","title":"Summarizing spoken and written conversations","year":2008,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Automatic summarization; Conversation; Computer science; Domain (mathematical analysis); Natural language processing; Open domain; Artificial intelligence; Information retrieval; World Wide Web; Linguistics; Question answering","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":[0.002363106,0.001674805,0.001048824,0.002801599,0.001100923,0.002740163,0.001115564,0.0009292539,0.004700406],"category_scores_gemma":[0.01506552,0.0004363937,0.0009556836,0.002137362,0.0003939652,0.003603752,0.001912656,0.001296958,0.002309917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005262535,"about_ca_system_score_gemma":0.0009143253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001869398,"about_ca_topic_score_gemma":0.002783769,"domain_scores_codex":[0.9963595,0.001454076,0.0002692347,0.0009959429,0.0007407672,0.0001804354],"domain_scores_gemma":[0.9924609,0.003976349,0.0006498768,0.001104691,0.00161461,0.0001936301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008452965,0.0001710958,0.004423499,0.00182275,0.0004152836,0.0003140192,0.004237013,0.01336828,0.06689553,0.007575722,0.01697593,0.8829555],"study_design_scores_gemma":[0.0001761576,0.001168837,0.02831057,0.0009746983,0.001779024,0.001081046,0.01127264,0.4958583,0.1448136,0.08498479,0.2290794,0.0005009431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08515335,0.006244746,0.8816041,0.001184307,0.0009519889,0.0007515071,0.004709775,0.007316843,0.01208344],"genre_scores_gemma":[0.4307773,0.002990989,0.5367715,0.000489292,0.001446396,0.0005854177,0.016022,0.0009673377,0.009949766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004700406,"threshold_uncertainty_score":0.01572436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03376233065434065,"score_gpt":0.2231904741633874,"score_spread":0.1894281435090467,"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."}}