{"id":"W2148404145","doi":"10.3115/v1/d14-1168","title":"Abstractive Summarization of Product Reviews Using Discourse Structure","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":200,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Automatic summarization; Computer science; Natural language processing; Product (mathematics); Linguistics; Artificial intelligence; Mathematics; Philosophy","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":[0.001897499,0.001884028,0.00130155,0.006441223,0.0007875125,0.00295382,0.001323418,0.0009060662,0.002957125],"category_scores_gemma":[0.01080162,0.0006405336,0.001068269,0.003065357,0.0003934974,0.003851225,0.001404253,0.001186086,0.002579065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007580305,"about_ca_system_score_gemma":0.001293221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002387718,"about_ca_topic_score_gemma":0.003325521,"domain_scores_codex":[0.9977144,0.0007214856,0.0002533766,0.0005553621,0.0006858599,0.00006954461],"domain_scores_gemma":[0.9927004,0.003334705,0.00112337,0.0005770989,0.002137166,0.0001272358],"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":[0.0003025225,0.0001741771,0.001546536,0.001827616,0.0002168757,0.0004304475,0.002566661,0.01340736,0.05523998,0.006826086,0.02538401,0.8920778],"study_design_scores_gemma":[0.0002170655,0.0006730478,0.007032035,0.0005131169,0.0009761709,0.000787688,0.002576258,0.6140401,0.1426519,0.04254547,0.1877068,0.0002803969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.033097,0.001708925,0.938297,0.0009850557,0.0002831234,0.0006986326,0.003331871,0.01762539,0.003973037],"genre_scores_gemma":[0.1132914,0.001142723,0.8689172,0.0002075729,0.000399634,0.000467379,0.01057252,0.0007954981,0.004205952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006441223,"threshold_uncertainty_score":0.01003504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01939090081418264,"score_gpt":0.326621961672267,"score_spread":0.3072310608580843,"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."}}