{"id":"W4411117117","doi":"10.18653/v1/w14-4407","title":"A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships","year":2014,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Automatic summarization; Computer science; Information retrieval; Natural language processing; Artificial intelligence; World Wide Web","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":[0.0006915923,0.0001079418,0.0001055964,0.00009894963,0.0003267305,0.0001998433,0.000247789,0.00006176583,0.000009511235],"category_scores_gemma":[0.000189282,0.0001049387,0.00002419913,0.0001741665,0.00002408256,0.0004654219,0.0001217847,0.0001892068,0.00001737398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002819665,"about_ca_system_score_gemma":0.00004045097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009020212,"about_ca_topic_score_gemma":0.00002457001,"domain_scores_codex":[0.9989282,0.0001244674,0.0002127603,0.000362537,0.000184231,0.000187822],"domain_scores_gemma":[0.998863,0.0005788635,0.00008523959,0.0003333874,0.00006001991,0.00007942885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002360541,0.0001239273,0.243562,0.0001735675,0.00006161675,0.00001105058,0.00618951,0.2868933,0.002295521,0.2179297,0.00285762,0.2398785],"study_design_scores_gemma":[0.0002482799,0.00001139724,0.007290954,0.00005298266,0.000003971416,0.000004393557,0.00007726237,0.9854957,0.0004700668,0.00233448,0.003848476,0.0001619638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02035712,0.00003707755,0.9570155,0.001316389,0.0001260977,0.00008597042,1.882597e-7,0.0002296289,0.02083204],"genre_scores_gemma":[0.8695121,0.000001421725,0.1294101,0.0003721608,0.00009877943,0.000005018719,0.000002301808,0.000008122795,0.0005899211],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.849155,"threshold_uncertainty_score":0.4279276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03280889997161753,"score_gpt":0.2284876458230432,"score_spread":0.1956787458514256,"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."}}