{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001253211,0.001532357,0.0008034824,0.001786977,0.0004400803,0.0009899338,0.001016335,0.0009086485,0.005077329],"category_scores_gemma":[0.004993861,0.0002993357,0.0008853143,0.0009870436,0.0003315232,0.002682524,0.000968122,0.001107579,0.002924669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003949328,"about_ca_system_score_gemma":0.000821636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001955003,"about_ca_topic_score_gemma":0.004142668,"domain_scores_codex":[0.9994986,0.0001428976,0.000053448,0.0001567056,0.0001116384,0.00003666934],"domain_scores_gemma":[0.998349,0.0006075653,0.00019817,0.0003119743,0.0004637361,0.00006954659],"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":[0.0007097663,0.0002805787,0.002378335,0.0006351155,0.0001628844,0.0003255877,0.0005298569,0.04047579,0.06892286,0.003707905,0.02655908,0.8553122],"study_design_scores_gemma":[0.0001827813,0.001221577,0.003803499,0.0001106249,0.0003989602,0.0004726179,0.0003605223,0.8480121,0.09188236,0.01170832,0.04174747,0.00009912714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08483502,0.003550905,0.8590682,0.001110404,0.000550736,0.000427666,0.005267986,0.04021068,0.00497839],"genre_scores_gemma":[0.4266279,0.001302568,0.5399891,0.000493061,0.0006956485,0.0004984554,0.01536746,0.001304938,0.01372092],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005077329,"threshold_uncertainty_score":0.01698536,"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."}}