{"id":"W2622583534","doi":"","title":"Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Natural Language Processing","topic":"Topic Modeling","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Submodular set function; Automatic summarization; Computer science; Redundancy (engineering); Sentence; Artificial intelligence; Natural language processing; Scalability; Set (abstract data type); Multi-document summarization; Information retrieval; Mathematics; Database","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001950475,0.000207695,0.0001624316,0.0001983901,0.0001587904,0.0003694396,0.0004013517,0.00008996203,0.00004328668],"category_scores_gemma":[0.0002082412,0.0001491633,0.00004752387,0.0001145869,0.00005668024,0.001056813,0.000172532,0.0002599893,0.000006968839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001887275,"about_ca_system_score_gemma":0.0001418579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009503811,"about_ca_topic_score_gemma":0.000007282894,"domain_scores_codex":[0.9983022,0.00005747566,0.0003004847,0.0005537341,0.0005487808,0.000237382],"domain_scores_gemma":[0.9989838,0.00008786094,0.0002489709,0.0002250304,0.0003720592,0.00008226778],"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":[0.00005625839,0.00007051471,0.0006215089,0.00004149441,0.00002755263,0.00004378054,0.0009015662,0.001704773,0.2636756,0.01469281,0.000002563792,0.7181616],"study_design_scores_gemma":[0.0005073443,0.00002435548,0.002034055,0.001243367,0.000008239055,0.00001330833,0.0001719121,0.9641856,0.02961582,0.001929439,0.00001858727,0.0002479635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.115165,0.0004405539,0.8816016,0.001773455,0.0006406585,0.0001184599,0.000004859,0.0001287206,0.0001266605],"genre_scores_gemma":[0.858716,0.000009902623,0.1407295,0.0003560815,0.00009630035,0.000008034259,0.000006684616,0.00001074346,0.00006674224],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9624808,"threshold_uncertainty_score":0.6082701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03583080388495406,"score_gpt":0.3049195049577958,"score_spread":0.2690887010728417,"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."}}