{"id":"W3157707325","doi":"10.5539/elt.v14n5p77","title":"Summarization in English as a Foreign Language: A Study Comparing Summary Performances to Summarizers’ Vocabulary Size","year":2021,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocabulary; Psychology; Language proficiency; Linguistics; Foreign language; Automatic summarization; Mathematics education; Computer science; Natural language processing","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":[0.002120995,0.0004446723,0.0004070272,0.0009127376,0.0002613151,0.001237967,0.0003138943,0.0003771517,0.001473535],"category_scores_gemma":[0.01807355,0.00016138,0.0003542452,0.0005204791,0.0003359845,0.001282978,0.0006208074,0.0003054838,0.0004379187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002120875,"about_ca_system_score_gemma":0.0002304061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008909158,"about_ca_topic_score_gemma":0.001036692,"domain_scores_codex":[0.9988149,0.0003444575,0.0001975898,0.0002125824,0.0003473971,0.00008303714],"domain_scores_gemma":[0.9857927,0.00617126,0.004669221,0.000548154,0.001974449,0.0008441139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008846497,0.0009987169,0.8665531,0.0005154277,0.0004081898,0.0007719128,0.02266273,0.0004275551,0.02486904,0.0001355175,0.0004765246,0.08129676],"study_design_scores_gemma":[0.00002282274,0.001741391,0.9873892,0.00004255529,0.0001115985,0.000360527,0.00598568,0.0005631636,0.002603882,0.0001042608,0.001044649,0.00003017938],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999199,0.00009228044,0.0001716073,0.00001060184,0.000002689275,0.00001006674,0.00002885057,0.000006519814,0.0004783277],"genre_scores_gemma":[0.9988306,0.0001122488,0.0003499241,0.00001597073,0.000009440124,0.00001412356,0.0001188843,0.000005249948,0.0005435744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002120995,"threshold_uncertainty_score":0.01121706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00830816340101185,"score_gpt":0.2700364138330177,"score_spread":0.2617282504320058,"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."}}