{"id":"W2250749132","doi":"10.3115/v1/w14-4318","title":"Extractive Summarization and Dialogue Act Modeling on Email Threads: An Integrated Probabilistic Approach","year":2014,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Automatic summarization; Computer science; Conversation; Task (project management); Probabilistic logic; Artificial intelligence; Natural language processing; Statistical model; Graphical model; Machine learning; Linguistics","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":[0.003062185,0.00132712,0.001319157,0.002541464,0.0006483878,0.001857014,0.002065054,0.001481025,0.001611902],"category_scores_gemma":[0.009084553,0.0008669267,0.001756863,0.001486265,0.0006391875,0.003425444,0.00150149,0.001736681,0.0009573277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008999758,"about_ca_system_score_gemma":0.001304152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00311474,"about_ca_topic_score_gemma":0.005438184,"domain_scores_codex":[0.997201,0.001197684,0.0002263669,0.0007708736,0.0004820049,0.0001220631],"domain_scores_gemma":[0.9937423,0.004091358,0.0007261631,0.0005371896,0.0007468634,0.0001561195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000538356,0.0003378584,0.005373668,0.0007736876,0.0005299416,0.0003161985,0.001511325,0.3428813,0.01784169,0.03195779,0.006731752,0.5912065],"study_design_scores_gemma":[0.00001302312,0.00005825368,0.000746986,0.00002077933,0.00006489193,0.00005231426,0.00005763752,0.9782172,0.001847793,0.0165299,0.00236838,0.00002266665],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005173787,0.0002827666,0.9929007,0.0001761123,0.00002463987,0.00005083669,0.0001544824,0.0009010303,0.0003356597],"genre_scores_gemma":[0.344034,0.0008521006,0.6480673,0.0002041889,0.0005212285,0.0005305656,0.001764655,0.000396028,0.003629918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00311474,"threshold_uncertainty_score":0.01619458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03787629057484056,"score_gpt":0.2470056146718206,"score_spread":0.20912932409698,"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."}}