{"id":"W2296617725","doi":"10.1109/icmla.2015.181","title":"Summary Sentence Classification Using Stylometry","year":2015,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stylometry; Automatic summarization; Computer science; Artificial intelligence; Natural language processing; Benchmark (surveying); Sentence; Set (abstract data type); Naive Bayes classifier; Task (project management); Correctness; Document classification; Information retrieval; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001192686,0.001059247,0.0008993179,0.004778323,0.000542522,0.001571551,0.0006956507,0.0006586654,0.003587394],"category_scores_gemma":[0.006260346,0.0001820233,0.0007419109,0.002098126,0.000300062,0.002034301,0.0007541587,0.0006755376,0.003173091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000442543,"about_ca_system_score_gemma":0.0006565283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007622259,"about_ca_topic_score_gemma":0.001548027,"domain_scores_codex":[0.9988952,0.0001885944,0.0001999241,0.0003015694,0.0003491421,0.00006557671],"domain_scores_gemma":[0.9962419,0.0009335172,0.0006647393,0.0004512296,0.001577261,0.0001313382],"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":[0.0004007498,0.00008900971,0.005384488,0.0004823641,0.0001046364,0.000212074,0.0003431185,0.006110667,0.04211745,0.002891424,0.01688012,0.9249838],"study_design_scores_gemma":[0.0001595665,0.001250523,0.03969918,0.0002367945,0.0005530333,0.00148192,0.001189666,0.6866723,0.1547815,0.02353523,0.09020843,0.0002319283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1230931,0.0032045,0.8368639,0.0006752871,0.0005669985,0.0008522052,0.008132498,0.01874677,0.007864716],"genre_scores_gemma":[0.4034078,0.001183071,0.5640384,0.0001535817,0.0006306519,0.0005091911,0.02211504,0.0005365747,0.00742571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004778323,"threshold_uncertainty_score":0.01200104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07811868697048134,"score_gpt":0.3263287666207434,"score_spread":0.2482100796502621,"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."}}