{"id":"W2905137389","doi":"10.1609/aaai.v33i01.33017476","title":"Generating Character Descriptions for Automatic Summarization of Fiction","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Character (mathematics); Computer science; Ranking (information retrieval); Information retrieval; Sample (material); Quality (philosophy); Natural language processing; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001154598,0.001333547,0.0006249206,0.007427599,0.0006229111,0.001370697,0.001058521,0.001006718,0.004350409],"category_scores_gemma":[0.01255134,0.0003660568,0.0006518243,0.003364647,0.0003116095,0.002730663,0.0009739005,0.0009220598,0.003609906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009125829,"about_ca_system_score_gemma":0.000827149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002397204,"about_ca_topic_score_gemma":0.004358114,"domain_scores_codex":[0.9986913,0.0004252737,0.0001565355,0.0003269552,0.0003024334,0.00009753284],"domain_scores_gemma":[0.9917105,0.004069051,0.001168964,0.0007932631,0.001935215,0.0003230772],"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":[0.0008906575,0.0004863247,0.02638099,0.003809482,0.0002159846,0.0009159098,0.002656762,0.01350938,0.03681326,0.005890962,0.1035137,0.8049166],"study_design_scores_gemma":[0.0003118874,0.001205895,0.05721996,0.000771889,0.000447416,0.002284284,0.005705757,0.5632731,0.09819743,0.01711977,0.253201,0.0002616588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.365172,0.006062247,0.4307535,0.002088282,0.0005520252,0.002916923,0.1350103,0.04294881,0.01449583],"genre_scores_gemma":[0.3233245,0.001054492,0.4504001,0.0001569125,0.0002370551,0.0009786256,0.2190239,0.0005636977,0.004260669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007427599,"threshold_uncertainty_score":0.01455361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0536322909089638,"score_gpt":0.2952677741446866,"score_spread":0.2416354832357228,"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."}}