{"id":"W3192616105","doi":"10.48550/arxiv.2108.03353","title":"Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Automatic summarization; Computer science; Bridging (networking); Phrase; Artificial intelligence; Modal; Semantics (computer science); Natural language processing; Mobile device; Set (abstract data type); Information retrieval; Human–computer interaction; World Wide Web; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002587279,0.0004052453,0.0003995343,0.0002977542,0.0003775938,0.000382105,0.001657016,0.0002894001,0.00008788604],"category_scores_gemma":[0.00007828058,0.0004506226,0.000156766,0.001127812,0.0001090314,0.0004377444,0.001875771,0.001281495,0.0001171866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002349823,"about_ca_system_score_gemma":0.0003547464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001710153,"about_ca_topic_score_gemma":0.0001064752,"domain_scores_codex":[0.997333,0.0003528011,0.0002531127,0.001471631,0.0001853423,0.0004040858],"domain_scores_gemma":[0.9973641,0.000200718,0.0004135027,0.001514166,0.0003076418,0.0001998739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006845533,0.0001204786,0.0218749,0.00008423332,0.00008478664,0.0001109843,0.0006955216,0.9642386,0.00005421828,0.00806846,0.00001166663,0.00464928],"study_design_scores_gemma":[0.0004532322,0.00007399375,0.01271686,0.0001694172,0.00006785114,0.000008787422,0.0003202469,0.9848832,0.0000414984,0.0006140777,0.0001400803,0.0005106854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.444041,0.00002382422,0.5541018,0.00005474928,0.00007423855,0.0003629199,0.000003174086,0.0006272988,0.0007109997],"genre_scores_gemma":[0.9572154,0.00003223753,0.04152432,0.00004666418,0.00005040815,0.0000160505,0.0001483138,0.00004079653,0.0009258614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5131744,"threshold_uncertainty_score":0.9997945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02825793177744283,"score_gpt":0.1912261793573584,"score_spread":0.1629682475799156,"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."}}