{"id":"W2103706669","doi":"10.5430/air.v2n3p35","title":"The role of statistical and semantic features in single-document extractive summarization","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Automatic summarization; Anaphora (linguistics); Sentence; Context (archaeology); Artificial intelligence; Word (group theory); Feature (linguistics); Term (time); Representation (politics); Resolution (logic); Information retrieval; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004319478,0.001056968,0.001349455,0.004125265,0.0005470625,0.002511965,0.0008260144,0.0005845891,0.002162331],"category_scores_gemma":[0.01416469,0.0003067155,0.0008977648,0.002800107,0.0004772439,0.004238613,0.0005489782,0.0007660569,0.001215156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004364825,"about_ca_system_score_gemma":0.0006376034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001090835,"about_ca_topic_score_gemma":0.001585323,"domain_scores_codex":[0.9976416,0.0009902577,0.0002713917,0.0003464408,0.0006481956,0.0001021544],"domain_scores_gemma":[0.9850487,0.01080839,0.001104668,0.0009017903,0.001986419,0.000150005],"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":[0.0008125384,0.0002632599,0.005132284,0.001126754,0.0002695906,0.0001101551,0.0002098341,0.01167794,0.04098809,0.00198644,0.002207711,0.9352154],"study_design_scores_gemma":[0.0002243112,0.004226542,0.07057118,0.0004858954,0.00202909,0.001131425,0.001627609,0.6681296,0.1955964,0.02672265,0.02880866,0.0004466679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2383966,0.008353319,0.7343176,0.00113409,0.0002589994,0.0005050491,0.002781712,0.008954512,0.005298132],"genre_scores_gemma":[0.6518652,0.001656994,0.3402133,0.0001125443,0.0003458774,0.000274612,0.003738208,0.0004298455,0.001363356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004319478,"threshold_uncertainty_score":0.02284384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04453799708544237,"score_gpt":0.3789329910890959,"score_spread":0.3343949940036536,"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."}}