{"id":"W2252230762","doi":"","title":"Using Syntactic and Shallow Semantic Kernels to Improve Multi-Modality Manifold-Ranking for Topic-Focused Multi-Document Summarization","year":2011,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Automatic summarization; Computer science; Ranking (information retrieval); Natural language processing; Cosine similarity; Relevance (law); Artificial intelligence; Information retrieval; Benchmark (surveying); Similarity (geometry); Semantic similarity; Modality (human–computer interaction); Pattern recognition (psychology); Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0004031088,0.0001867674,0.0002095401,0.0001014572,0.0001610651,0.0001936769,0.0004133115,0.00007855376,0.00001478033],"category_scores_gemma":[0.0000808629,0.0001762161,0.00005835497,0.000124304,0.00001031838,0.0006551571,0.0003313741,0.00007420998,0.000005327907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000965193,"about_ca_system_score_gemma":0.00004047456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001395109,"about_ca_topic_score_gemma":0.0003205147,"domain_scores_codex":[0.9984172,0.000047092,0.0003460019,0.0006442775,0.000182963,0.0003624909],"domain_scores_gemma":[0.9990727,0.00006694497,0.00009828704,0.0005259325,0.0001028374,0.0001333239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000161854,0.001250048,0.1204779,0.001473428,0.0004337013,0.00007890195,0.04266213,0.00446484,0.1748233,0.3430218,0.00001971479,0.3111323],"study_design_scores_gemma":[0.00089015,0.00005998629,0.008358125,0.00004511472,0.00002425269,0.000005237346,0.00006243779,0.9782879,0.009740424,0.002234783,0.00001978843,0.0002718353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1320861,0.00001880445,0.866329,0.0002172186,0.0003747638,0.0007512159,0.000001250264,0.0001265897,0.00009507859],"genre_scores_gemma":[0.5461219,0.000001385782,0.453486,0.0001596117,0.00002793091,0.00002150802,5.476156e-7,0.000009098158,0.0001720219],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.973823,"threshold_uncertainty_score":0.7185884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1150379468243564,"score_gpt":0.3097517739301193,"score_spread":0.1947138271057628,"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."}}