{"id":"W2399623965","doi":"","title":"Generate Compressed Sentences with Stanford Typed Dependencies towards Abstractive Summarization.","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Computer science; Sentence; Natural language processing; Ranking (information retrieval); Natural language generation; Artificial intelligence; Selection (genetic algorithm); Metric (unit); Process (computing); Natural language; Programming language","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":[0.001259063,0.001160545,0.0005227059,0.001274188,0.0004202514,0.0008275518,0.0008984062,0.000664543,0.005762292],"category_scores_gemma":[0.006431627,0.00029191,0.0006592357,0.0009533987,0.0002753571,0.00145444,0.0008989199,0.000903362,0.003181611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003256047,"about_ca_system_score_gemma":0.0008044005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008478586,"about_ca_topic_score_gemma":0.00192276,"domain_scores_codex":[0.9989321,0.0004652219,0.00008886548,0.0001983981,0.0002754911,0.00004002461],"domain_scores_gemma":[0.9964684,0.0016243,0.0003069666,0.000550705,0.0009609211,0.00008874444],"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":[0.0004790437,0.0003942201,0.00172598,0.001305122,0.000214856,0.0006801297,0.001023594,0.03154831,0.1464329,0.01959489,0.05442725,0.7421738],"study_design_scores_gemma":[0.000175195,0.000847843,0.00330814,0.0001322129,0.0003240317,0.0007456403,0.0005618812,0.6329046,0.2144612,0.05449926,0.09189116,0.0001488047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02128097,0.0003932122,0.9637286,0.0004638195,0.0002273202,0.0004482254,0.002664428,0.007988401,0.002805039],"genre_scores_gemma":[0.1131983,0.0002823079,0.8697649,0.0002232196,0.0001666409,0.0004827446,0.0104798,0.0006149267,0.004787222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005762292,"threshold_uncertainty_score":0.01927674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324165590593292,"score_gpt":0.2320686903214139,"score_spread":0.208827034415481,"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."}}