{"id":"W4387183694","doi":"10.1111/coin.12603","title":"A semantically enhanced text retrieval framework with abstractive summarization","year":2023,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Barrie Urology Group; York University","funders":"China Scholarship Council; Natural Science Foundation of Hubei Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Hubei Provincial Department of Education","keywords":"Computer science; Automatic summarization; Natural language processing; Artificial intelligence; Question answering; Encoder; Language model; Information retrieval; Generative grammar; Transformer; Semantics (computer science); Sequence (biology); Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000238504,0.0001376502,0.0001307759,0.0001542742,0.0001418513,0.0001671117,0.0005943907,0.00007635392,0.00003100621],"category_scores_gemma":[0.000252481,0.0001268997,0.00003703246,0.00124136,0.00006900868,0.0004129898,0.0001611365,0.0002217489,0.0005661353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004982779,"about_ca_system_score_gemma":0.000156305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005322008,"about_ca_topic_score_gemma":0.000001773535,"domain_scores_codex":[0.9983935,0.00004038874,0.0002773311,0.0004850722,0.0005410565,0.0002625968],"domain_scores_gemma":[0.9983805,0.0007852574,0.0001007098,0.0003046529,0.0003421502,0.00008676643],"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":[0.00002008988,0.00002477435,0.00009542298,0.0000107908,0.00001602262,0.00001767744,0.0009186579,0.6718003,0.0001344057,0.3081692,0.00003733964,0.01875535],"study_design_scores_gemma":[0.00004193276,0.00005404736,0.002732469,0.00006418805,0.000002948728,0.000008379169,0.00005990433,0.7757339,0.003083401,0.2180222,0.00004345908,0.0001531133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02067447,0.0000146329,0.9766853,0.0009227115,0.0002386283,0.0001616141,0.000001930084,0.0003802165,0.0009204919],"genre_scores_gemma":[0.7653726,0.00000723497,0.2341906,0.0002031342,0.00006113586,0.000006521554,0.0000125381,0.000009070177,0.0001372004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7446981,"threshold_uncertainty_score":0.7276713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02991899628109718,"score_gpt":0.299368928543697,"score_spread":0.2694499322625998,"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."}}