{"id":"W2060686","doi":"10.63317/47puorupsub8","title":"Automatic Summarization Using Terminological and Semantic Resources","year":2010,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Automatic summarization; Computer science; Natural language processing; Information retrieval; Artificial intelligence","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":[0.0002033355,0.000151983,0.0002011361,0.0001081558,0.00008212957,0.0003895675,0.0006342743,0.0003130314,0.00002191345],"category_scores_gemma":[0.00005746007,0.000124017,0.00003387851,0.00005682857,0.00004427719,0.0001215229,0.002214099,0.0004324884,0.000004301619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001779999,"about_ca_system_score_gemma":0.00004613185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001094035,"about_ca_topic_score_gemma":0.00001520522,"domain_scores_codex":[0.9988481,0.0000528001,0.0002395436,0.000513942,0.0001747514,0.0001708785],"domain_scores_gemma":[0.9990888,0.00005527117,0.0001152812,0.0006511746,0.00003373154,0.00005571757],"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":[0.000002908821,0.0001562685,0.02430237,0.001330269,0.00009844219,0.0001361881,0.006052558,0.01270346,0.007687713,0.09799297,0.0001005032,0.8494363],"study_design_scores_gemma":[0.00004682395,0.000005711393,0.002471888,0.00006375929,0.000008554973,0.00002550447,0.000006106088,0.9833795,0.0001103718,0.01367126,0.00005844516,0.000152065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5028124,0.00003335223,0.4960717,0.0001977053,0.0002502261,0.0000932128,2.147082e-7,0.0002005575,0.0003406495],"genre_scores_gemma":[0.6779431,0.000005412871,0.3218089,0.00008633308,0.00006478935,0.000003338015,0.000001147856,0.000004839458,0.00008211518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9706761,"threshold_uncertainty_score":0.5057266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04781014630795609,"score_gpt":0.2754713633292548,"score_spread":0.2276612170212987,"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."}}