{"id":"W4388306831","doi":"10.23977/acss.2023.070903","title":"Research on NLP Based Automatic Summarization Generation Method for Medical Texts","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Computer science; Natural language processing; Similarity (geometry); Task (project management); Artificial intelligence; Sentence; Information retrieval; Domain (mathematical analysis); Semantic similarity; Documentation; Deep learning; Generative grammar","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004182012,0.0001032368,0.0002087266,0.0003524705,0.000147717,0.0002242038,0.0004085586,0.00008791863,0.000001923913],"category_scores_gemma":[0.00008610305,0.00008876467,0.00002727621,0.000607899,0.00002017377,0.000342639,0.0001145533,0.0001271181,0.000008144505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003847381,"about_ca_system_score_gemma":0.00007685344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001993519,"about_ca_topic_score_gemma":0.00001584636,"domain_scores_codex":[0.9976531,0.0005333174,0.0003907749,0.0004761536,0.0006533794,0.0002932415],"domain_scores_gemma":[0.9979012,0.001523188,0.00006701642,0.0003063089,0.0001187749,0.00008352613],"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":[0.000002676104,0.00002683895,0.0001343926,0.000192234,0.000004448975,0.00001144327,0.000212539,0.4836413,0.0001415219,0.0352217,0.0005285399,0.4798824],"study_design_scores_gemma":[0.0003412167,0.0001272401,0.00006939884,0.0002100019,8.115593e-7,0.000003095215,0.00001115852,0.9950654,0.0001422206,0.00222525,0.001706474,0.00009774834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003140954,0.0006081956,0.9938335,0.0006859765,0.001052078,0.0004847938,0.000001897556,0.0001361973,0.00005642417],"genre_scores_gemma":[0.6800982,0.0001288144,0.3179961,0.0003988149,0.0009299668,0.0003328804,0.00002597007,0.00001867176,0.00007054983],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6769573,"threshold_uncertainty_score":0.3619719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1084951229147702,"score_gpt":0.4202254896988056,"score_spread":0.3117303667840354,"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."}}