{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CJTEWBL225EFQWEJ57KXEOIOW4","short_pith_number":"pith:CJTEWBL2","schema_version":"1.0","canonical_sha256":"12664b057ad748585889efd572390eb71a2fe3f153b31df2a2030f53ff989c7c","source":{"kind":"arxiv","id":"2406.15267","version":2},"attestation_state":"computed","paper":{"title":"Evaluating Diversity in Automatic Poetry Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hannes Gr\\\"oner, Sina Zarrie{\\ss}, Steffen Eger, Yanran Chen","submitted_at":"2024-06-21T16:03:21Z","abstract_excerpt":"Natural Language Generation (NLG), and more generally generative AI, are among the currently most impactful research fields. Creative NLG, such as automatic poetry generation, is a fascinating niche in this area. While most previous research has focused on forms of the Turing test when evaluating automatic poetry generation -- can humans distinguish between automatic and human generated poetry -- we evaluate the diversity of automatically generated poetry (with a focus on quatrains), by comparing distributions of generated poetry to distributions of human poetry along structural, lexical, sema"},"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":"2406.15267","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-21T16:03:21Z","cross_cats_sorted":[],"title_canon_sha256":"bb56bdf2e82bb875d05881be609261c448ef3a5e312047133abf8d2af7b67aea","abstract_canon_sha256":"c20e957dd086a116ae9cb3f2c2fd7e1f0c5f4dbfd96fcff9f015c454a8c260d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:45.291778Z","signature_b64":"5BH8zo/dpFTVZ6EzKg8//Rcy3lIH3vWW3nKiWQJ8OLWQa7H4bSaLKhb/nBrf4B5hE+MpGSdTRlrKYH6fzEoIAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12664b057ad748585889efd572390eb71a2fe3f153b31df2a2030f53ff989c7c","last_reissued_at":"2026-07-05T09:32:45.291260Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:45.291260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Diversity in Automatic Poetry Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hannes Gr\\\"oner, Sina Zarrie{\\ss}, Steffen Eger, Yanran Chen","submitted_at":"2024-06-21T16:03:21Z","abstract_excerpt":"Natural Language Generation (NLG), and more generally generative AI, are among the currently most impactful research fields. Creative NLG, such as automatic poetry generation, is a fascinating niche in this area. While most previous research has focused on forms of the Turing test when evaluating automatic poetry generation -- can humans distinguish between automatic and human generated poetry -- we evaluate the diversity of automatically generated poetry (with a focus on quatrains), by comparing distributions of generated poetry to distributions of human poetry along structural, lexical, sema"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.15267","kind":"arxiv","version":2},"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/2406.15267/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":"2406.15267","created_at":"2026-07-05T09:32:45.291325+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.15267v2","created_at":"2026-07-05T09:32:45.291325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.15267","created_at":"2026-07-05T09:32:45.291325+00:00"},{"alias_kind":"pith_short_12","alias_value":"CJTEWBL225EF","created_at":"2026-07-05T09:32:45.291325+00:00"},{"alias_kind":"pith_short_16","alias_value":"CJTEWBL225EFQWEJ","created_at":"2026-07-05T09:32:45.291325+00:00"},{"alias_kind":"pith_short_8","alias_value":"CJTEWBL2","created_at":"2026-07-05T09:32:45.291325+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/CJTEWBL225EFQWEJ57KXEOIOW4","json":"https://pith.science/pith/CJTEWBL225EFQWEJ57KXEOIOW4.json","graph_json":"https://pith.science/api/pith-number/CJTEWBL225EFQWEJ57KXEOIOW4/graph.json","events_json":"https://pith.science/api/pith-number/CJTEWBL225EFQWEJ57KXEOIOW4/events.json","paper":"https://pith.science/paper/CJTEWBL2"},"agent_actions":{"view_html":"https://pith.science/pith/CJTEWBL225EFQWEJ57KXEOIOW4","download_json":"https://pith.science/pith/CJTEWBL225EFQWEJ57KXEOIOW4.json","view_paper":"https://pith.science/paper/CJTEWBL2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.15267&json=true","fetch_graph":"https://pith.science/api/pith-number/CJTEWBL225EFQWEJ57KXEOIOW4/graph.json","fetch_events":"https://pith.science/api/pith-number/CJTEWBL225EFQWEJ57KXEOIOW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CJTEWBL225EFQWEJ57KXEOIOW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CJTEWBL225EFQWEJ57KXEOIOW4/action/storage_attestation","attest_author":"https://pith.science/pith/CJTEWBL225EFQWEJ57KXEOIOW4/action/author_attestation","sign_citation":"https://pith.science/pith/CJTEWBL225EFQWEJ57KXEOIOW4/action/citation_signature","submit_replication":"https://pith.science/pith/CJTEWBL225EFQWEJ57KXEOIOW4/action/replication_record"}},"created_at":"2026-07-05T09:32:45.291325+00:00","updated_at":"2026-07-05T09:32:45.291325+00:00"}