{"id":"W2758238851","doi":"10.18653/v1/w17-4502","title":"Multimedia Summary Generation from Online Conversations: Current Approaches and Future Directions","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Asynchronous communication; Variety (cybernetics); Visualization; Conversation; Multimedia; Representation (politics); Human–computer interaction; Domain (mathematical analysis); Social media; Key (lock); World Wide Web; Data visualization; Space (punctuation); Data science; Artificial intelligence","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":[0.01553888,0.002025925,0.001901446,0.004726666,0.001212419,0.01409691,0.008132052,0.003589744,0.01794541],"category_scores_gemma":[0.03288458,0.001279796,0.001628547,0.004271882,0.002783416,0.01805956,0.003953249,0.002855812,0.005678722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00152363,"about_ca_system_score_gemma":0.001952132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004163843,"about_ca_topic_score_gemma":0.003155824,"domain_scores_codex":[0.9946026,0.002802717,0.0003647655,0.0009695785,0.001046378,0.0002139829],"domain_scores_gemma":[0.9562643,0.02971536,0.001150587,0.004625349,0.006629062,0.001615497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003428155,0.0002985225,0.001700186,0.004024039,0.00009832441,0.0001014637,0.003143319,0.002960444,0.004853814,0.04361651,0.02009374,0.9187668],"study_design_scores_gemma":[0.0002791786,0.0006278854,0.004726647,0.006621011,0.0003334919,0.0008693154,0.01609069,0.1301751,0.01922589,0.1990413,0.621452,0.0005574992],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01690726,0.1738025,0.7347558,0.03449909,0.001081359,0.0008567146,0.001714507,0.01258899,0.02379383],"genre_scores_gemma":[0.0897043,0.07952839,0.8151439,0.001825008,0.001624072,0.0009569763,0.002726331,0.001338383,0.00715272],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01794541,"threshold_uncertainty_score":0.08217847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09814234575045262,"score_gpt":0.3213934764486184,"score_spread":0.2232511306981658,"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."}}