{"id":"W1558536082","doi":"10.1007/978-3-319-30671-1_27","title":"Multi-document Summarization Based on Atomic Semantic Events and Their Temporal Relationships","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Automatic summarization; Computer science; Novelty; Natural language processing; Event (particle physics); Sentence; Information retrieval; Artificial intelligence; Domain (mathematical analysis); Precision and recall; Set (abstract data type); Multi-document summarization; Salient","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009915264,0.0004125828,0.0003292429,0.0006564612,0.0003013911,0.0002518123,0.001374027,0.0002423024,0.000006943381],"category_scores_gemma":[0.000100677,0.0003074264,0.00007077163,0.000241305,0.0002084297,0.000468951,0.0005962259,0.0005227501,0.0000246483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949873,"about_ca_system_score_gemma":0.0003279412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001087785,"about_ca_topic_score_gemma":0.00004335466,"domain_scores_codex":[0.9972155,0.00008039509,0.0004294004,0.001307312,0.0005612168,0.0004061401],"domain_scores_gemma":[0.9978256,0.0005612135,0.0002414453,0.00112466,0.0001114577,0.0001356612],"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":[0.00001808094,0.00007861137,0.007483702,0.0001069727,0.00002188664,0.00004146935,0.001502361,0.1085688,0.000303834,0.05272559,0.000009304508,0.8291394],"study_design_scores_gemma":[0.0003619197,0.00007004994,0.0007164343,0.0005505413,0.000002967997,0.000008354373,8.78138e-8,0.9024567,0.00019052,0.09516905,0.0001240087,0.0003493392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005950998,0.00009538818,0.9963242,0.001253886,0.000903,0.0004100999,0.000003762174,0.0001183848,0.0002962106],"genre_scores_gemma":[0.7117941,0.00001063805,0.2871704,0.0005976215,0.0001360794,0.000006906841,0.000004202471,0.00002188282,0.0002581013],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8287901,"threshold_uncertainty_score":0.9999378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02955753619847791,"score_gpt":0.2456908215947669,"score_spread":0.216133285396289,"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."}}