{"id":"W7015767590","doi":"","title":"TESA: A task in entity semantic aggregation for abstractive automatic summarization","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Task (project management); Automatic summarization; Semantics (computer science); Feature (linguistics); Semantic feature","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":[0.001063589,0.0006021532,0.0007285024,0.0006485769,0.0006049059,0.0003487418,0.001085373,0.0007195735,0.00003628061],"category_scores_gemma":[0.001537698,0.0007205296,0.000305042,0.00101814,0.00002073239,0.002079179,0.0001843232,0.0009739823,0.00005996833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009641925,"about_ca_system_score_gemma":0.0002035438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004514879,"about_ca_topic_score_gemma":0.004374041,"domain_scores_codex":[0.9955499,0.0003073647,0.001152548,0.001509926,0.000812899,0.0006674125],"domain_scores_gemma":[0.9968367,0.0004489398,0.0008477862,0.001053207,0.0006417542,0.000171635],"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":[0.00004871644,0.0005172245,0.0001554031,0.001907039,0.000207717,0.0001131864,0.0001527913,0.001428193,0.02472005,0.1977179,0.000005028241,0.7730268],"study_design_scores_gemma":[0.005871017,0.0004158144,0.02098073,0.007293097,0.0006008924,0.00008866552,0.001129609,0.4170182,0.1879374,0.3454393,0.007821393,0.005403839],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843476,0.0002802724,0.002074609,0.00005024346,0.002759671,0.001982063,0.0002438372,0.0004880065,0.007773667],"genre_scores_gemma":[0.9740091,0.00007605435,0.02160038,0.0001193707,0.00005774314,0.000426508,0.001386661,0.000101852,0.002222315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7676229,"threshold_uncertainty_score":0.9995246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0165575354271419,"score_gpt":0.2519691149439467,"score_spread":0.2354115795168048,"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."}}