{"id":"W2407606031","doi":"","title":"Experimenting with Clause Segmentation for Text Summarization.","year":2008,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Computer science; Segmentation; Sentence; Natural language processing; Artificial intelligence; Dependent clause; Selection (genetic algorithm); Heuristic; Baseline (sea)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.006470132,0.001510178,0.001141294,0.001561751,0.001185741,0.001596325,0.001596624,0.00161612,0.004618608],"category_scores_gemma":[0.02759119,0.0004923661,0.0007497268,0.002653689,0.0004756781,0.002994495,0.0008219155,0.00177519,0.002690972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006019289,"about_ca_system_score_gemma":0.0007614488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003783325,"about_ca_topic_score_gemma":0.007115012,"domain_scores_codex":[0.9945326,0.00272788,0.0004899061,0.001221167,0.0008195665,0.0002088562],"domain_scores_gemma":[0.9740292,0.01891508,0.0009252785,0.001792206,0.003926256,0.0004120212],"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":[0.003665883,0.002400807,0.005764124,0.004184051,0.001156559,0.0006688716,0.003438044,0.04019149,0.1947392,0.001438539,0.03825304,0.7040994],"study_design_scores_gemma":[0.00242324,0.01403832,0.01754612,0.0001541808,0.001457917,0.001670535,0.003360445,0.3909908,0.4963283,0.004404775,0.06724915,0.0003762449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.591708,0.00402019,0.3301883,0.001471328,0.001031709,0.003527993,0.009923428,0.04812807,0.01000096],"genre_scores_gemma":[0.3842095,0.0005500721,0.5771877,0.000544038,0.0002731221,0.001037591,0.0279591,0.001730028,0.006508871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006470132,"threshold_uncertainty_score":0.03421772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005151985847622,"score_gpt":0.2657650424146443,"score_spread":0.2557135225561681,"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."}}