{"id":"W4360764250","doi":"10.1109/icmla55696.2022.00030","title":"A Robust Approach to Fine-tune Pre-trained Transformer-based models for Text Summarization through Latent Space Compression","year":2022,"lang":"en","type":"article","venue":"","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; Encoder; Transformer; Autoencoder; Inference; Artificial intelligence; Deep learning; Speech recognition; Pattern recognition (psychology)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002931034,0.0001767653,0.0002027411,0.00009545537,0.0003516448,0.00009599899,0.0007580537,0.00004757996,0.00002910202],"category_scores_gemma":[0.00001036711,0.0001596623,0.0001005396,0.0004293762,0.00001140137,0.0004551166,0.0001517357,0.0001274917,0.000001719129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009990147,"about_ca_system_score_gemma":0.0001037967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001339336,"about_ca_topic_score_gemma":0.00001758355,"domain_scores_codex":[0.9982275,0.00006734597,0.0002968263,0.0006185625,0.0004363492,0.0003534495],"domain_scores_gemma":[0.9991856,0.00006861697,0.00006222356,0.000518196,0.00007338803,0.00009195443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003931164,0.0001710485,0.000009721335,0.00003392691,0.000006731779,3.808204e-7,0.001692277,0.9144765,0.00119859,0.07724448,0.0008801922,0.004246857],"study_design_scores_gemma":[0.0008964821,0.0001443521,0.00002184292,0.00001143052,0.00000750022,0.000002003355,0.00005510654,0.9911826,0.001774284,0.003928909,0.001755669,0.0002197656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00225675,0.00002569064,0.9879901,0.003900793,0.0001903891,0.001333617,0.00001628108,0.0002778301,0.00400854],"genre_scores_gemma":[0.485688,7.166897e-7,0.5121707,0.0005990642,0.00002721084,0.0003658908,0.0000433769,0.00001433915,0.001090737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4834312,"threshold_uncertainty_score":0.6510839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07704970137916824,"score_gpt":0.2582683224406403,"score_spread":0.1812186210614721,"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."}}