{"id":"W4391968356","doi":"10.1142/s0218213024500052","title":"Summary Augmenter: A Text Augmentation Framework to Improve Summarization Quality","year":2024,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Automatic summarization; Computer science; Quality (philosophy); Information retrieval; Philosophy; Epistemology","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001169186,0.0001585607,0.0001942212,0.0004610636,0.00005912212,0.001408303,0.001416876,0.00008695543,0.0001082744],"category_scores_gemma":[0.0007120224,0.0001479134,0.0001773365,0.000403993,0.00003144278,0.001834376,0.000238244,0.0003361506,0.0001818993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003084392,"about_ca_system_score_gemma":0.0002189144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004270593,"about_ca_topic_score_gemma":0.00001486048,"domain_scores_codex":[0.9971609,0.0001062825,0.001177446,0.0003509752,0.0009874793,0.0002169157],"domain_scores_gemma":[0.998152,0.0004795313,0.0002812001,0.000273641,0.0006692625,0.0001444175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005710365,0.00006433164,0.00005103311,0.00001083413,0.00008282102,0.00007386891,0.001456487,0.005038141,0.009829069,0.27863,0.0003229302,0.7043833],"study_design_scores_gemma":[0.0001125769,0.0005464639,0.0004030633,0.001226602,0.00004134863,0.0001532603,0.0009105039,0.3286007,0.1524534,0.5018863,0.01298724,0.0006785381],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01647392,0.0001775415,0.9670502,0.007940815,0.007747482,0.000160653,0.00001392831,0.00007504653,0.0003604286],"genre_scores_gemma":[0.8994869,0.00006949696,0.0978146,0.000999908,0.001386419,0.00001065171,0.000006275145,0.00001571115,0.0002100676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.883013,"threshold_uncertainty_score":0.9996283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07811631283339375,"score_gpt":0.3859162627930595,"score_spread":0.3077999499596657,"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."}}