{"id":"W4299697650","doi":"10.1007/978-3-031-01880-0_4","title":"Summarizing Text Conversations","year":2011,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on data management","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Natural language processing; Linguistics; Philosophy","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":[0.001092017,0.001231323,0.0005278677,0.003668195,0.002674967,0.004994449,0.001000948,0.0009862422,0.1080403],"category_scores_gemma":[0.009821166,0.0005153288,0.0004615918,0.002964865,0.0009678321,0.006606237,0.002845801,0.001961308,0.05504644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158886,"about_ca_system_score_gemma":0.00113519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913583,"about_ca_topic_score_gemma":0.002268753,"domain_scores_codex":[0.9985386,0.0006035158,0.00008478641,0.0002878107,0.000383013,0.0001022454],"domain_scores_gemma":[0.9969831,0.001339996,0.0001498841,0.0004746697,0.0009244133,0.0001279883],"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":[0.0001926373,0.00004227437,0.0004106806,0.0003628078,0.00002265338,0.0002264052,0.004503747,0.0006915368,0.004133561,0.1540418,0.4984479,0.3369239],"study_design_scores_gemma":[0.00001286672,0.00002007975,0.0004348539,0.0002858712,0.00002417393,0.000118844,0.002124269,0.002725708,0.003699755,0.06723307,0.9232952,0.0000252115],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01390174,0.006654597,0.3404176,0.0191527,0.01079107,0.0005847549,0.01899474,0.01926013,0.5702428],"genre_scores_gemma":[0.2095647,0.005319401,0.1466483,0.002789343,0.005523974,0.0008582744,0.04118906,0.0107712,0.5773357],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1080403,"threshold_uncertainty_score":0.3614306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05137841069642054,"score_gpt":0.2638444147118555,"score_spread":0.2124660040154349,"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."}}