{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006124051,0.00006845606,0.00006839354,0.00003922166,0.0003614309,0.0004453781,0.0003023528,0.00003044064,0.00002616744],"category_scores_gemma":[0.00003452605,0.00005854584,0.00001835488,0.00004742711,0.00004284113,0.0007470161,0.0001553433,0.00005370145,0.00001254546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009971131,"about_ca_system_score_gemma":0.00002628939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008071272,"about_ca_topic_score_gemma":0.0002315172,"domain_scores_codex":[0.9994746,0.00002272076,0.0001079121,0.0002200687,0.0001047115,0.00006995067],"domain_scores_gemma":[0.999355,0.00001924569,0.00007459189,0.0004464499,0.00004148137,0.00006324452],"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":[9.120243e-7,0.0002089064,0.004847941,0.000005377127,0.00002892559,9.278861e-7,0.0007939461,0.000009407157,0.00009556648,0.1167782,0.02100917,0.8562207],"study_design_scores_gemma":[0.0001786967,0.000004857469,0.01366862,0.000003892317,0.000007822419,4.189804e-7,0.00005843546,0.7635012,0.00008702531,0.0003102256,0.2220965,0.00008231294],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006021094,0.001840276,0.9757422,0.01168086,0.002439211,0.00019184,0.0002135309,0.0002403997,0.001630543],"genre_scores_gemma":[0.247283,0.02027869,0.7075344,0.002065115,0.01023202,0.00004951268,0.007118634,0.00004452247,0.005394044],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8561383,"threshold_uncertainty_score":0.4294791,"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."}}