{"id":"W2476992958","doi":"10.1007/978-3-319-41754-7_46","title":"Automatic Text Summarization with a Reduced Vocabulary Using Continuous Space Vectors","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Automatic summarization; Computer science; Vocabulary; Natural language processing; Artificial intelligence; Context (archaeology); State (computer science); Space (punctuation); Information retrieval; Speech recognition; Algorithm; Linguistics","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":[0.0007037524,0.001746843,0.001716887,0.003198621,0.0007892908,0.001943848,0.001126205,0.001050415,0.006910965],"category_scores_gemma":[0.002534172,0.0004472873,0.001486831,0.002867,0.0003511809,0.002478829,0.00162438,0.001407924,0.007688212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003501138,"about_ca_system_score_gemma":0.0009844472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00245614,"about_ca_topic_score_gemma":0.00286384,"domain_scores_codex":[0.9987984,0.0002389099,0.0001513998,0.0003983125,0.0002915076,0.0001215256],"domain_scores_gemma":[0.9987572,0.0003590031,0.00008525722,0.0001825556,0.0005599483,0.0000559546],"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":[0.0006371153,0.0001399638,0.0003251343,0.000683622,0.0001405249,0.0001648093,0.0002123024,0.004724362,0.1123683,0.002588116,0.02868491,0.8493308],"study_design_scores_gemma":[0.0003366015,0.001333562,0.004500234,0.0002688847,0.0009089626,0.0009532448,0.001265249,0.719545,0.1533173,0.01873046,0.09862638,0.0002140916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02736607,0.003318204,0.9460103,0.0003888619,0.0009757391,0.0004412095,0.004207931,0.01433361,0.002958198],"genre_scores_gemma":[0.175628,0.002069918,0.7780552,0.0001916204,0.0009994456,0.0008058351,0.0279821,0.001287194,0.01298069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006910965,"threshold_uncertainty_score":0.02311951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571219957677657,"score_gpt":0.2318069900887686,"score_spread":0.216094790511992,"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."}}