{"id":32264,"date":"2026-01-21T17:01:36","date_gmt":"2026-01-21T17:01:36","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/pm3-kie-a-probabilistic-multi-task-meta-model-for-document-key-information-extraction\/"},"modified":"2026-06-08T13:19:03","modified_gmt":"2026-06-08T13:19:03","slug":"pm3-kie-a-probabilistic-multi-task-meta-model-for-document-key-information-extraction","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/pm3-kie-a-probabilistic-multi-task-meta-model-for-document-key-information-extraction\/","title":{"rendered":"{PM}3-{KIE}: A Probabilistic Multi-Task Meta-Model for Document Key Information Extraction"},"content":{"rendered":"<p>Key Information Extraction ({KIE}) from visually rich documents is commonly approached as either fine-grained token classification or coarse-grained entity extraction. While token-level models capture spatial and visual cues, entity-level models better represent logical dependencies and align with real-world use cases.We introduce {PM}3-{KIE}, a probabilistic multi-task meta-model that incorporates both fine-grained and coarse-grained models. It serves as a lightweight reasoning layer that jointly predicts entities and all appearances in a document. {PM}3-{KIE} incorporates domain-specific schema constraints to enforce logical consistency and integrates large language models for semantic validation, thereby reducing extraction errors.Experiments on two public datasets, {DeepForm} and {FARA}, show that {PM}3-{KIE} outperforms three state-of-the-art models and a stacked ensemble, achieving a statistically significant 2\\% improvement in F1 score.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key Information Extraction ({KIE}) from visually rich documents is commonly approached as either fine-grained token classification or coarse-grained entity extraction. While token-level models capture spatial and visual cues, entity-level models better represent logical dependencies and align with real-world use cases.We introduce {PM}3-{KIE}, a probabilistic multi-task meta-model that incorporates both fine-grained and coarse-grained models. It serves as a lightweight reasoning layer that jointly predicts entities and all appearances in a document. [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32264","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32264","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32264\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32264"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32264"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}