{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001624922,0.001035872,0.001476611,0.002233268,0.0006500522,0.001132763,0.001037478,0.001132895,0.001351125],"category_scores_gemma":[0.004675192,0.0002931863,0.0009634671,0.001790256,0.0005046714,0.003056458,0.000989732,0.001101803,0.001163032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005852274,"about_ca_system_score_gemma":0.0007493776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00198422,"about_ca_topic_score_gemma":0.004093708,"domain_scores_codex":[0.9989492,0.0004380157,0.00008371883,0.0001748211,0.0002572885,0.000096962],"domain_scores_gemma":[0.9978293,0.000685504,0.0002464886,0.0004503364,0.0006912568,0.00009722189],"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":[0.0004012093,0.0004819283,0.002508361,0.0002837358,0.0002795749,0.0001721747,0.0003555657,0.09844514,0.072988,0.01316132,0.007986335,0.8029367],"study_design_scores_gemma":[0.00002233462,0.0001772686,0.001030102,0.00000839625,0.00005211619,0.00007719023,0.00006667637,0.9757902,0.01135261,0.009988368,0.001390062,0.00004469129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0355908,0.0004750217,0.961202,0.000127562,0.00004221819,0.00006098442,0.0001085278,0.001830881,0.0005619968],"genre_scores_gemma":[0.5327194,0.0003520269,0.4622371,0.0001251621,0.0001666752,0.0001508255,0.001458894,0.0002855437,0.00250421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002233268,"threshold_uncertainty_score":0.008593559,"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."}}