{"id":"W4312217620","doi":"10.18280/ria.360516","title":"Improving Extractive Text Summarization Performance Using Enhanced Feature Based RBM Method","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Computer science; Discriminative model; Artificial intelligence; Feature (linguistics); Feature selection; Restricted Boltzmann machine; Set (abstract data type); Word (group theory); Natural language processing; Sentence; Feature extraction; Topic model; Multi-document summarization; Process (computing); Information retrieval; Artificial neural network; Pattern recognition (psychology)","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":[0.0008676738,0.000196434,0.0002056461,0.0001972615,0.0007335096,0.0001435446,0.0009696444,0.00006674969,0.0001639604],"category_scores_gemma":[0.0001009531,0.0002208931,0.00009603118,0.0009454708,0.00002807862,0.0006019753,0.0004345669,0.0004749923,0.00003194392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002634797,"about_ca_system_score_gemma":0.00017635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007091754,"about_ca_topic_score_gemma":0.000003730028,"domain_scores_codex":[0.9979167,0.0002056792,0.0003700369,0.0007242066,0.0003632625,0.0004201274],"domain_scores_gemma":[0.9985059,0.0002086637,0.0002534205,0.0008173313,0.0001323496,0.00008232195],"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":[0.00001004383,0.00005088131,0.00004549063,0.00002596662,0.000004590864,0.000004966563,0.0009114689,0.7379889,0.07190077,0.002188659,0.00001683441,0.1868514],"study_design_scores_gemma":[0.00003401633,0.00006262299,0.00001160311,0.00002201488,0.000006601352,0.00001755267,0.0002655478,0.776131,0.2223081,0.0002651292,0.0006727814,0.0002029902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02390622,0.0001167187,0.973647,0.0003465507,0.0006500419,0.0003019107,0.000004051509,0.0001542016,0.0008732964],"genre_scores_gemma":[0.7302017,0.000004526032,0.2687773,0.0002057943,0.00006988006,0.00003787398,0.000006421818,0.00001716016,0.0006793113],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7062955,"threshold_uncertainty_score":0.900776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04148329245521411,"score_gpt":0.2896778044399188,"score_spread":0.2481945119847047,"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."}}