{"id":"W108365020","doi":"10.1007/978-3-642-25631-8_35","title":"An Aspect-Driven Random Walk Model for Topic-Focused Multi-document Summarization","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Automatic summarization; Computer science; Random walk; Multi-document summarization; Exploit; Task (project management); Information retrieval; Markov chain; Artificial intelligence; Natural language processing; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007684963,0.000536609,0.0005751441,0.0005767696,0.0002904586,0.0005257479,0.003622906,0.0003572682,0.00001366198],"category_scores_gemma":[0.00006753858,0.0004989224,0.0001716957,0.0002373277,0.0002318255,0.001028587,0.0007398242,0.0004659777,0.00001110273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003194727,"about_ca_system_score_gemma":0.0005069275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004258945,"about_ca_topic_score_gemma":0.0002043537,"domain_scores_codex":[0.9960532,0.00003950814,0.0006372252,0.001861976,0.0007215347,0.0006865242],"domain_scores_gemma":[0.9970955,0.0002250491,0.0003049113,0.001835111,0.0003229613,0.0002164913],"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":[0.00001794822,0.00004346999,0.0000309508,0.00003393138,0.00001198358,0.0000110283,0.00211856,0.4695162,0.000168101,0.05981882,0.000005332312,0.4682237],"study_design_scores_gemma":[0.0009950992,0.0001165397,0.00001285154,0.0001055838,0.0000101441,0.000005982733,7.077313e-8,0.8197069,0.0006246188,0.1777917,0.0001505291,0.0004799826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006566442,0.0001235074,0.9962682,0.0003064468,0.001411584,0.001046218,0.000006250059,0.0002286937,0.0005434047],"genre_scores_gemma":[0.1164343,0.00002340939,0.881786,0.0007146954,0.0003594075,0.00004188999,0.00001183074,0.00004000894,0.0005884975],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4677437,"threshold_uncertainty_score":0.9997463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03908626929132147,"score_gpt":0.2656497139802484,"score_spread":0.2265634446889269,"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."}}