{"id":"W4214722770","doi":"10.24124/2018/58991","title":"A graph-based approach towards automatic text summarization.","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Automatic summarization; Computer science; Information retrieval; Graph; Text graph; Multi-document summarization; The Internet; Natural language processing; Text processing; Artificial intelligence; World Wide Web; Theoretical computer science","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":[0.0008340576,0.001530932,0.0008906438,0.004464305,0.0005543769,0.00139031,0.001317282,0.0009904447,0.003086336],"category_scores_gemma":[0.002845199,0.0004308,0.001568987,0.003091445,0.0003851832,0.001529963,0.0007793899,0.001107861,0.002460082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006747404,"about_ca_system_score_gemma":0.000784099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00435169,"about_ca_topic_score_gemma":0.006950276,"domain_scores_codex":[0.9990977,0.0002692089,0.00006607865,0.0002843933,0.000242394,0.00004020218],"domain_scores_gemma":[0.9988385,0.0004894406,0.0001421989,0.0001717664,0.0003174965,0.00004076356],"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":[0.0001465167,0.00015721,0.0005869085,0.001076107,0.0003120159,0.0002279111,0.0003489781,0.07865518,0.03204929,0.01465449,0.02949791,0.8422875],"study_design_scores_gemma":[0.00005809803,0.0002356101,0.001171995,0.00007817154,0.0002107112,0.000348417,0.000204562,0.8805173,0.01736264,0.04653866,0.05321184,0.00006192252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002012504,0.001084135,0.9903414,0.0001633504,0.0000817742,0.0001757097,0.0006930603,0.00446149,0.0009864065],"genre_scores_gemma":[0.03833218,0.0009267576,0.9522464,0.0001222545,0.0001472532,0.0002676576,0.003818709,0.0004301094,0.003708712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004464305,"threshold_uncertainty_score":0.01032478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02013488987031178,"score_gpt":0.2567149650776157,"score_spread":0.2365800752073039,"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."}}