{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:THYW2S4GSHQU5ETTVR3PD7SYAX","short_pith_number":"pith:THYW2S4G","canonical_record":{"source":{"id":"2206.03021","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-06-07T05:30:46Z","cross_cats_sorted":[],"title_canon_sha256":"7fcbda61fb55765219085b5112c30b5cc080129d7657aa35be2752b13bfa945b","abstract_canon_sha256":"22cfbfcb19799c58410aed6ab0d701affd9012dea4e4114ab5a2b690bc4c296a"},"schema_version":"1.0"},"canonical_sha256":"99f16d4b8691e14e9273ac76f1fe5805d16b36687467dce5a57fb2dbdb91ff51","source":{"kind":"arxiv","id":"2206.03021","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03021","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03021v1","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03021","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"pith_short_12","alias_value":"THYW2S4GSHQU","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"pith_short_16","alias_value":"THYW2S4GSHQU5ETT","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"pith_short_8","alias_value":"THYW2S4G","created_at":"2026-07-05T04:29:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:THYW2S4GSHQU5ETTVR3PD7SYAX","target":"record","payload":{"canonical_record":{"source":{"id":"2206.03021","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-06-07T05:30:46Z","cross_cats_sorted":[],"title_canon_sha256":"7fcbda61fb55765219085b5112c30b5cc080129d7657aa35be2752b13bfa945b","abstract_canon_sha256":"22cfbfcb19799c58410aed6ab0d701affd9012dea4e4114ab5a2b690bc4c296a"},"schema_version":"1.0"},"canonical_sha256":"99f16d4b8691e14e9273ac76f1fe5805d16b36687467dce5a57fb2dbdb91ff51","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:29:38.450520Z","signature_b64":"o9qYbRd846o7Aknp4UNO8keeRReTw08K1C7gjJItVj/PfDG9zMr/4qGGCKlTvX3VdPQlHzKMfFnZZqkH3I4zBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99f16d4b8691e14e9273ac76f1fe5805d16b36687467dce5a57fb2dbdb91ff51","last_reissued_at":"2026-07-05T04:29:38.450113Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:29:38.450113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.03021","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:29:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ixxaWddOuciHIcfFGlYM6wshFi2nd7S6SokvMImtsNH6PZGPxHhRRRJ11ETyboNsyY2btXaWVOCFyzOZs+npBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T19:43:37.392755Z"},"content_sha256":"1a6415ddd7fbf1c22bf5142b6aacae4b434d93ff7898c33ea1dfe4f37f1dfce5","schema_version":"1.0","event_id":"sha256:1a6415ddd7fbf1c22bf5142b6aacae4b434d93ff7898c33ea1dfe4f37f1dfce5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:THYW2S4GSHQU5ETTVR3PD7SYAX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Plot Writing From Pre-Trained Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dittaya Wanvarie, Vishakha Kadam, Yiping Jin","submitted_at":"2022-06-07T05:30:46Z","abstract_excerpt":"Pre-trained language models (PLMs) fail to generate long-form narrative text because they do not consider global structure. As a result, the generated texts are often incohesive, repetitive, or lack content. Recent work in story generation reintroduced explicit content planning in the form of prompts, keywords, or semantic frames. Trained on large parallel corpora, these models can generate more logical event sequences and thus more contentful stories. However, these intermediate representations are often not in natural language and cannot be utilized by PLMs without fine-tuning. We propose ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03021","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/2206.03021/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:29:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oZ7rZ/oBRbzDIPN3BBQFN+A8ZdrHn+00HWbZ9ckUjusmunYVIVduMWdyIBhKPFt183LiXBIryhdmiDIPlucLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T19:43:37.393242Z"},"content_sha256":"84b81f05bb3ccd07de16ebbbca3d4ca9a5330eceb29bdeeee66eb255f627f914","schema_version":"1.0","event_id":"sha256:84b81f05bb3ccd07de16ebbbca3d4ca9a5330eceb29bdeeee66eb255f627f914"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/THYW2S4GSHQU5ETTVR3PD7SYAX/bundle.json","state_url":"https://pith.science/pith/THYW2S4GSHQU5ETTVR3PD7SYAX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/THYW2S4GSHQU5ETTVR3PD7SYAX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T19:43:37Z","links":{"resolver":"https://pith.science/pith/THYW2S4GSHQU5ETTVR3PD7SYAX","bundle":"https://pith.science/pith/THYW2S4GSHQU5ETTVR3PD7SYAX/bundle.json","state":"https://pith.science/pith/THYW2S4GSHQU5ETTVR3PD7SYAX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/THYW2S4GSHQU5ETTVR3PD7SYAX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:THYW2S4GSHQU5ETTVR3PD7SYAX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"22cfbfcb19799c58410aed6ab0d701affd9012dea4e4114ab5a2b690bc4c296a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-06-07T05:30:46Z","title_canon_sha256":"7fcbda61fb55765219085b5112c30b5cc080129d7657aa35be2752b13bfa945b"},"schema_version":"1.0","source":{"id":"2206.03021","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03021","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03021v1","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03021","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"pith_short_12","alias_value":"THYW2S4GSHQU","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"pith_short_16","alias_value":"THYW2S4GSHQU5ETT","created_at":"2026-07-05T04:29:38Z"},{"alias_kind":"pith_short_8","alias_value":"THYW2S4G","created_at":"2026-07-05T04:29:38Z"}],"graph_snapshots":[{"event_id":"sha256:84b81f05bb3ccd07de16ebbbca3d4ca9a5330eceb29bdeeee66eb255f627f914","target":"graph","created_at":"2026-07-05T04:29:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2206.03021/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained language models (PLMs) fail to generate long-form narrative text because they do not consider global structure. As a result, the generated texts are often incohesive, repetitive, or lack content. Recent work in story generation reintroduced explicit content planning in the form of prompts, keywords, or semantic frames. Trained on large parallel corpora, these models can generate more logical event sequences and thus more contentful stories. However, these intermediate representations are often not in natural language and cannot be utilized by PLMs without fine-tuning. We propose ge","authors_text":"Dittaya Wanvarie, Vishakha Kadam, Yiping Jin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-06-07T05:30:46Z","title":"Plot Writing From Pre-Trained Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03021","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1a6415ddd7fbf1c22bf5142b6aacae4b434d93ff7898c33ea1dfe4f37f1dfce5","target":"record","created_at":"2026-07-05T04:29:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"22cfbfcb19799c58410aed6ab0d701affd9012dea4e4114ab5a2b690bc4c296a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-06-07T05:30:46Z","title_canon_sha256":"7fcbda61fb55765219085b5112c30b5cc080129d7657aa35be2752b13bfa945b"},"schema_version":"1.0","source":{"id":"2206.03021","kind":"arxiv","version":1}},"canonical_sha256":"99f16d4b8691e14e9273ac76f1fe5805d16b36687467dce5a57fb2dbdb91ff51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"99f16d4b8691e14e9273ac76f1fe5805d16b36687467dce5a57fb2dbdb91ff51","first_computed_at":"2026-07-05T04:29:38.450113Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:29:38.450113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"o9qYbRd846o7Aknp4UNO8keeRReTw08K1C7gjJItVj/PfDG9zMr/4qGGCKlTvX3VdPQlHzKMfFnZZqkH3I4zBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:29:38.450520Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.03021","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1a6415ddd7fbf1c22bf5142b6aacae4b434d93ff7898c33ea1dfe4f37f1dfce5","sha256:84b81f05bb3ccd07de16ebbbca3d4ca9a5330eceb29bdeeee66eb255f627f914"],"state_sha256":"57e4bd135e29ad85d1c544a493ba3dbb62b33a3d25198114447c26547c60ec15"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F7NWIr4jehGav8Ywe9OfJeVP2irllraq9yS6Hv3yD1ZY8C4X/KTHDb8vDHG4IZV3xI7JQWw76vrENyzVCeklCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T19:43:37.396791Z","bundle_sha256":"ca2f76ffaf824d1529ff8d90e8d7ef108cfd49e1ae4e6de550e061ef439b6dfc"}}