{"id":"W4385574022","doi":"10.18653/v1/2022.emnlp-main.694","title":"Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of the Fraser Valley; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Institute for Computing, Information and Cognitive Systems","keywords":"Automatic summarization; Computer science; Transformer; Segmentation; Artificial intelligence; Pipeline (software); Machine learning; Engineering; Voltage","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001929002,0.00003688711,0.00005578575,0.000117006,0.00005310965,0.00002441404,0.0001188219,0.0000101864,0.00004634638],"category_scores_gemma":[0.00000566744,0.00004130268,0.00001067178,0.00012758,0.000003742932,0.0003624557,0.0001524071,0.00003765932,3.016813e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004948671,"about_ca_system_score_gemma":0.000007760843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002968897,"about_ca_topic_score_gemma":0.00006934276,"domain_scores_codex":[0.9993951,0.00006278745,0.0001999895,0.0001493241,0.0001315886,0.00006118371],"domain_scores_gemma":[0.9997716,0.00001012198,0.00006493359,0.0001221155,0.00002071486,0.00001052701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004593128,0.0001546674,0.6278932,0.0001076941,0.00001449555,0.000003428554,0.00971008,0.01743314,0.1313098,0.1449587,0.0001256904,0.06828443],"study_design_scores_gemma":[0.0008790949,0.0001331287,0.1956341,0.00002874218,0.000005166899,0.000004464672,0.001700692,0.7877119,0.004949263,0.008762788,0.00002937415,0.0001612839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5353263,0.000004826036,0.4641914,0.00009072676,0.00005520618,0.00007058362,3.626129e-7,0.00001457098,0.0002459789],"genre_scores_gemma":[0.9907352,0.00000175869,0.008888106,0.00006458181,0.000005109293,0.00002673695,0.00001079662,0.000002178161,0.0002654815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7702788,"threshold_uncertainty_score":0.1684275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02972887344622373,"score_gpt":0.2780526593413482,"score_spread":0.2483237858951245,"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."}}