{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KK3INE57OIBHG5DDKSAPCKJVJB","short_pith_number":"pith:KK3INE57","schema_version":"1.0","canonical_sha256":"52b68693bf72027374635480f12935484f5557ba5c9af1b607ca6410288d2b52","source":{"kind":"arxiv","id":"2411.15666","version":1},"attestation_state":"computed","paper":{"title":"Ontology-Constrained Generation of Domain-Specific Clinical Summaries","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Amal Zouaq, Gaya Mehenni","submitted_at":"2024-11-23T23:05:48Z","abstract_excerpt":"Large Language Models (LLMs) offer promising solutions for text summarization. However, some domains require specific information to be available in the summaries. Generating these domain-adapted summaries is still an open challenge. Similarly, hallucinations in generated content is a major drawback of current approaches, preventing their deployment. This study proposes a novel approach that leverages ontologies to create domain-adapted summaries both structured and unstructured. We employ an ontology-guided constrained decoding process to reduce hallucinations while improving relevance. When "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.15666","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-23T23:05:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"20741b8b382d54d15ae4e047d23acb04a609f6159fb880157c824fb7c53efc9c","abstract_canon_sha256":"c96a2719ded2ae5077dfb717754fa67acf7d5f283d74ae29173cb871333f7b66"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:39:38.894209Z","signature_b64":"hxUyX7hPOSe92lu+0ukuNtozHyCOfX7kBHKZ4Uh4IAbjpt4FSMZ9XJOqES0DfuqFAO8gYquCU6bzIIGnzEDqBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52b68693bf72027374635480f12935484f5557ba5c9af1b607ca6410288d2b52","last_reissued_at":"2026-07-05T09:39:38.893687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:39:38.893687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ontology-Constrained Generation of Domain-Specific Clinical Summaries","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Amal Zouaq, Gaya Mehenni","submitted_at":"2024-11-23T23:05:48Z","abstract_excerpt":"Large Language Models (LLMs) offer promising solutions for text summarization. However, some domains require specific information to be available in the summaries. Generating these domain-adapted summaries is still an open challenge. Similarly, hallucinations in generated content is a major drawback of current approaches, preventing their deployment. This study proposes a novel approach that leverages ontologies to create domain-adapted summaries both structured and unstructured. We employ an ontology-guided constrained decoding process to reduce hallucinations while improving relevance. When "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15666","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.15666/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.15666","created_at":"2026-07-05T09:39:38.893752+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15666v1","created_at":"2026-07-05T09:39:38.893752+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15666","created_at":"2026-07-05T09:39:38.893752+00:00"},{"alias_kind":"pith_short_12","alias_value":"KK3INE57OIBH","created_at":"2026-07-05T09:39:38.893752+00:00"},{"alias_kind":"pith_short_16","alias_value":"KK3INE57OIBHG5DD","created_at":"2026-07-05T09:39:38.893752+00:00"},{"alias_kind":"pith_short_8","alias_value":"KK3INE57","created_at":"2026-07-05T09:39:38.893752+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB","json":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB.json","graph_json":"https://pith.science/api/pith-number/KK3INE57OIBHG5DDKSAPCKJVJB/graph.json","events_json":"https://pith.science/api/pith-number/KK3INE57OIBHG5DDKSAPCKJVJB/events.json","paper":"https://pith.science/paper/KK3INE57"},"agent_actions":{"view_html":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB","download_json":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB.json","view_paper":"https://pith.science/paper/KK3INE57","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15666&json=true","fetch_graph":"https://pith.science/api/pith-number/KK3INE57OIBHG5DDKSAPCKJVJB/graph.json","fetch_events":"https://pith.science/api/pith-number/KK3INE57OIBHG5DDKSAPCKJVJB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB/action/storage_attestation","attest_author":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB/action/author_attestation","sign_citation":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB/action/citation_signature","submit_replication":"https://pith.science/pith/KK3INE57OIBHG5DDKSAPCKJVJB/action/replication_record"}},"created_at":"2026-07-05T09:39:38.893752+00:00","updated_at":"2026-07-05T09:39:38.893752+00:00"}