{"id":"W65944014","doi":"10.1609/icwsm.v3i1.13980","title":"Regression-Based Summarization of Email Conversations","year":2009,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Topic Modeling","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Automatic summarization; Computer science; Artificial intelligence; Regression; Binary classification; Natural language processing; Machine learning; Random forest; Regression analysis; Recall; Support vector machine; Statistics; Mathematics","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":[0.003141782,0.001529209,0.001361318,0.004354623,0.0006408338,0.00150255,0.00117963,0.0009477834,0.002218169],"category_scores_gemma":[0.01765988,0.000376567,0.0008715882,0.002361852,0.0002653019,0.002433066,0.0008488909,0.001511978,0.003271863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006476454,"about_ca_system_score_gemma":0.0005877679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001951439,"about_ca_topic_score_gemma":0.002335179,"domain_scores_codex":[0.9966543,0.00141344,0.0002632855,0.0008168421,0.0006736053,0.000178464],"domain_scores_gemma":[0.9890103,0.005029781,0.001398985,0.0009776346,0.003367493,0.0002158652],"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":[0.0006744264,0.0002667542,0.006954894,0.0007374614,0.0002292446,0.0001629156,0.0008892493,0.04072216,0.04092926,0.002396878,0.01237886,0.8936579],"study_design_scores_gemma":[0.00002834039,0.0003079603,0.01166593,0.00008983206,0.0001643031,0.0001670663,0.0004261309,0.9384579,0.02950425,0.006642498,0.01246284,0.00008292181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07558882,0.001685021,0.9027947,0.0005024555,0.0003444437,0.0003831105,0.002462615,0.0133144,0.002924414],"genre_scores_gemma":[0.4727352,0.0008652466,0.5042418,0.0001642138,0.0007250186,0.0005242136,0.01300308,0.001144138,0.006597139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004354623,"threshold_uncertainty_score":0.01661551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03218593799524793,"score_gpt":0.2552067750714423,"score_spread":0.2230208370761944,"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."}}