{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007222759,0.0009568768,0.001190407,0.001577914,0.0004164341,0.000694733,0.0009185086,0.0007011099,0.001984179],"category_scores_gemma":[0.001766603,0.0002392736,0.0009106086,0.001012361,0.0002230414,0.001218484,0.0004159218,0.0006943816,0.001741009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004553476,"about_ca_system_score_gemma":0.0007108377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002870924,"about_ca_topic_score_gemma":0.003844185,"domain_scores_codex":[0.9993499,0.0001284028,0.00007858001,0.0001570459,0.0002203356,0.00006568436],"domain_scores_gemma":[0.9991522,0.0002259169,0.0001252572,0.00009184985,0.0003683936,0.00003635008],"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":[0.0004200602,0.0002333238,0.001515638,0.000374006,0.000127948,0.0003080246,0.0002140385,0.05315784,0.1163171,0.001532802,0.008598448,0.8172009],"study_design_scores_gemma":[0.00007355743,0.0003715884,0.002912374,0.00002347116,0.000132704,0.0002984522,0.0001096841,0.9174858,0.06873268,0.001708427,0.008106472,0.00004472926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06326697,0.002887624,0.9213893,0.0003485254,0.0002465438,0.0001737179,0.0005780797,0.008756666,0.00235244],"genre_scores_gemma":[0.3965396,0.001176434,0.5862473,0.0002158409,0.0003457525,0.0003612837,0.003130957,0.0004170953,0.01156569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002870924,"threshold_uncertainty_score":0.006637752,"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."}}