{"id":"W7104370506","doi":"10.18653/v1/2025.newsum-main.2","title":"Hierarchical Attention Adapter for Abstractive Dialogue Summarization","year":2025,"lang":"","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Adapter (computing); Key (lock); Redundancy (engineering)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001640648,0.001646443,0.001126076,0.001517778,0.000656969,0.001123745,0.002060439,0.001364329,0.007216149],"category_scores_gemma":[0.005067232,0.0003854474,0.001050023,0.001112093,0.0004877295,0.002657464,0.002273321,0.002201035,0.004509491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075425,"about_ca_system_score_gemma":0.001226575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006271385,"about_ca_topic_score_gemma":0.01057482,"domain_scores_codex":[0.998768,0.0004758831,0.00007827301,0.000371722,0.0001898175,0.0001163498],"domain_scores_gemma":[0.9986832,0.000559253,0.00008358589,0.0003426793,0.0002427014,0.00008860118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006210638,0.0002689012,0.001280254,0.0006147191,0.0001759177,0.0001738012,0.0009223867,0.03643175,0.02891353,0.007077483,0.03039726,0.8931229],"study_design_scores_gemma":[0.000155619,0.0005460984,0.001936438,0.00007640506,0.0002294684,0.0002043349,0.0005944259,0.8964337,0.0354652,0.02269177,0.04159026,0.00007625017],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03672819,0.00234893,0.8907737,0.0006181401,0.0002894436,0.0003451001,0.001767655,0.06141433,0.005714608],"genre_scores_gemma":[0.5160893,0.0009283892,0.4495187,0.001126328,0.0004487178,0.0008534915,0.01195925,0.001916645,0.0171593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007216149,"threshold_uncertainty_score":0.02414042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02747302771307453,"score_gpt":0.2800566222989719,"score_spread":0.2525835945858974,"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."}}