{"id":"W4389518188","doi":"10.18653/v1/2023.banglalp-1.10","title":"BanglaCHQ-Summ: An Abstractive Summarization Dataset for Medical Queries in Bangla Conversational Speech","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Queen's University","funders":"","keywords":"Automatic summarization; Computer science; Popularity; Natural language processing; Bengali; Parsing; Artificial intelligence; Information retrieval; Process (computing); Psychology","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":[0.001451308,0.001604371,0.0008398403,0.002768172,0.001431542,0.001256509,0.00159954,0.002075566,0.009911063],"category_scores_gemma":[0.006622786,0.0003080986,0.001003792,0.002289259,0.000626375,0.001343435,0.00183777,0.001254615,0.01421603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001596326,"about_ca_system_score_gemma":0.002037588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01763843,"about_ca_topic_score_gemma":0.02841081,"domain_scores_codex":[0.9974859,0.0007904925,0.0004777609,0.00054313,0.0005240546,0.0001786149],"domain_scores_gemma":[0.9961351,0.001543834,0.0002406899,0.0005298958,0.001239579,0.0003108705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001468338,0.00103183,0.01288465,0.006311647,0.0002703674,0.001578847,0.002663017,0.004013172,0.03165251,0.001855146,0.7740357,0.1622349],"study_design_scores_gemma":[0.0007218707,0.0009001849,0.0909204,0.0005956612,0.0002660905,0.002864754,0.006187119,0.03840428,0.02945116,0.003137884,0.8261515,0.0003990151],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07252111,0.002142929,0.01203783,0.001569527,0.0005356944,0.001651245,0.8874777,0.01092367,0.01114028],"genre_scores_gemma":[0.0325866,0.0002250569,0.01359689,0.0002328673,0.0000755106,0.0008337206,0.9490068,0.0001934298,0.003249088],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01763843,"threshold_uncertainty_score":0.03507155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03633922986558311,"score_gpt":0.313978566736106,"score_spread":0.2776393368705229,"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."}}