{"id":"W2109071706","doi":"","title":"Fully Abstractive Approach to Guided Summarization","year":2012,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Automatic summarization; Computer science; Abstraction; Selection (genetic algorithm); Natural language generation; Context (archaeology); Natural language processing; Artificial intelligence; Natural language; Information retrieval","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":[0.002177868,0.001211136,0.0009356786,0.002061502,0.0008699822,0.00222233,0.001986859,0.0009814323,0.005329366],"category_scores_gemma":[0.005513597,0.0004802814,0.00114087,0.00148064,0.0012299,0.002903861,0.002218373,0.002024427,0.00198331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006495413,"about_ca_system_score_gemma":0.001026467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00104512,"about_ca_topic_score_gemma":0.002228773,"domain_scores_codex":[0.9969077,0.001350676,0.0002563433,0.0004432405,0.0008864417,0.0001556223],"domain_scores_gemma":[0.9958898,0.001797975,0.0003352823,0.001059193,0.0008234085,0.0000942634],"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":[0.0003542458,0.0001803276,0.0004345272,0.001851623,0.000280855,0.0004375607,0.002081135,0.04259052,0.05373137,0.1747502,0.02296467,0.700343],"study_design_scores_gemma":[0.0001131506,0.0004782246,0.0008173175,0.0003022524,0.0003441762,0.0004639461,0.0004030983,0.2908583,0.05307195,0.4376817,0.2153023,0.0001635811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001902858,0.0005067708,0.993126,0.0002086715,0.00006849709,0.0001308381,0.0002278979,0.001588602,0.002239823],"genre_scores_gemma":[0.08613984,0.0009155177,0.9026897,0.000443906,0.0002277472,0.00049351,0.001765734,0.0005455733,0.00677838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005329366,"threshold_uncertainty_score":0.01782852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02562072193849277,"score_gpt":0.2896314398022767,"score_spread":0.2640107178637839,"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."}}