{"id":"W3217119495","doi":"10.1109/access.2021.3129786","title":"A Survey of Automatic Text Summarization: Progress, Process and Challenges","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Topic Modeling","field":"Computer Science","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Automatic summarization; Computer science; Workflow; Taxonomy (biology); Information retrieval; Domain (mathematical analysis); Text graph; Process (computing); The Internet; Feature extraction; Data science; Artificial intelligence; World Wide Web; Database","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":[0.003191533,0.001635508,0.001622571,0.006435175,0.0009030075,0.003586992,0.001801436,0.001133537,0.004003617],"category_scores_gemma":[0.01146407,0.0006147355,0.001034933,0.007287225,0.0007933763,0.006973457,0.001318959,0.001495371,0.005749554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008163887,"about_ca_system_score_gemma":0.001678028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001823129,"about_ca_topic_score_gemma":0.002000682,"domain_scores_codex":[0.9973521,0.0007518843,0.0003232077,0.0005882853,0.0008658837,0.000118706],"domain_scores_gemma":[0.9904498,0.005258782,0.0006344308,0.0008806717,0.002599399,0.0001768593],"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":[0.00009286791,0.00006457427,0.0009071591,0.004245624,0.00006817807,0.00005752311,0.0004277577,0.00245451,0.006437891,0.00526428,0.03098379,0.9489958],"study_design_scores_gemma":[0.00006715802,0.0007240675,0.008548386,0.004079306,0.0004901664,0.001136892,0.003106437,0.09542143,0.05223214,0.03746977,0.7964034,0.0003209349],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0201966,0.3552785,0.5826624,0.007613952,0.001729659,0.0005894207,0.004307852,0.01183846,0.01578322],"genre_scores_gemma":[0.1040323,0.3580292,0.4943269,0.002489408,0.005653371,0.0007447583,0.01862855,0.002238023,0.01385756],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006435175,"threshold_uncertainty_score":0.0168786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08943312503184098,"score_gpt":0.3288187108396128,"score_spread":0.2393855858077719,"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."}}