{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:DJTXDAFVMOPW3EX2JUWBFFFVEE","short_pith_number":"pith:DJTXDAFV","canonical_record":{"source":{"id":"1909.00734","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-02T14:29:36Z","cross_cats_sorted":[],"title_canon_sha256":"4f6bbe77204ea512bba7a625de30aaea27b1dfa7dc375365c33f09ced3252eba","abstract_canon_sha256":"6ef6dd4e5b1bb7ce6ca028b872a332e8e416d68d06ff6e849f175909e9effd4d"},"schema_version":"1.0"},"canonical_sha256":"1a677180b5639f6d92fa4d2c1294b5213bf5c01f399c678968f88681638960ce","source":{"kind":"arxiv","id":"1909.00734","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.00734","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"arxiv_version","alias_value":"1909.00734v1","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.00734","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"pith_short_12","alias_value":"DJTXDAFVMOPW","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"pith_short_16","alias_value":"DJTXDAFVMOPW3EX2","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"pith_short_8","alias_value":"DJTXDAFV","created_at":"2026-07-05T00:01:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:DJTXDAFVMOPW3EX2JUWBFFFVEE","target":"record","payload":{"canonical_record":{"source":{"id":"1909.00734","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-02T14:29:36Z","cross_cats_sorted":[],"title_canon_sha256":"4f6bbe77204ea512bba7a625de30aaea27b1dfa7dc375365c33f09ced3252eba","abstract_canon_sha256":"6ef6dd4e5b1bb7ce6ca028b872a332e8e416d68d06ff6e849f175909e9effd4d"},"schema_version":"1.0"},"canonical_sha256":"1a677180b5639f6d92fa4d2c1294b5213bf5c01f399c678968f88681638960ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:01:12.633845Z","signature_b64":"/2qEOX18ahya9/Zk83CO0ILPMhppyNtB7xLlNPH0jCmfICjSLvfNYaF0WnGtE73DoS30n3OxlVq+uhZNqWAQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a677180b5639f6d92fa4d2c1294b5213bf5c01f399c678968f88681638960ce","last_reissued_at":"2026-07-05T00:01:12.633461Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:01:12.633461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.00734","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":"2026-07-05T00:01:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"To6qV9ApN4rZCXO86xkquc0k6FAxhCMODAqFtabw8GR39h+Uv3esmm1OUKXsl6PKyjTWgGGL9lTPLUdVg4dIBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T17:18:09.477052Z"},"content_sha256":"90c2ff3bd665d6bde1c788a2402f84fd61636b5e4f0ae0efe17824049d2a0d11","schema_version":"1.0","event_id":"sha256:90c2ff3bd665d6bde1c788a2402f84fd61636b5e4f0ae0efe17824049d2a0d11"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:DJTXDAFVMOPW3EX2JUWBFFFVEE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sentence-Level Content Planning and Style Specification for Neural Text Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Lu Wang, Xinyu Hua","submitted_at":"2019-09-02T14:29:36Z","abstract_excerpt":"Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems. Recent all-in-one style neural generation models have made impressive progress, yet they often produce outputs that are incoherent and unfaithful to the input. To address these issues, we present an end-to-end trained two-step generation model, where a sentence-level content planner first decides on the keyphrases to cover as well as a desired language style, followed by a surface realization decoder t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.00734","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/1909.00734/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":"2026-07-05T00:01:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QchAzF9SViBcHLSJtTWg5IAf3s0L8tbizgNvrBS+3sI0vRQL8ku+olASGT3/4cwJj9f8CQtsn8v/k3P7dhJRDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T17:18:09.477589Z"},"content_sha256":"af6342f5efb602cf3cd6354f65164a3a48a398ddd845f9e5b7307196c32cbd00","schema_version":"1.0","event_id":"sha256:af6342f5efb602cf3cd6354f65164a3a48a398ddd845f9e5b7307196c32cbd00"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DJTXDAFVMOPW3EX2JUWBFFFVEE/bundle.json","state_url":"https://pith.science/pith/DJTXDAFVMOPW3EX2JUWBFFFVEE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DJTXDAFVMOPW3EX2JUWBFFFVEE/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":"2026-08-16T17:18:09Z","links":{"resolver":"https://pith.science/pith/DJTXDAFVMOPW3EX2JUWBFFFVEE","bundle":"https://pith.science/pith/DJTXDAFVMOPW3EX2JUWBFFFVEE/bundle.json","state":"https://pith.science/pith/DJTXDAFVMOPW3EX2JUWBFFFVEE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DJTXDAFVMOPW3EX2JUWBFFFVEE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:DJTXDAFVMOPW3EX2JUWBFFFVEE","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":"6ef6dd4e5b1bb7ce6ca028b872a332e8e416d68d06ff6e849f175909e9effd4d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-02T14:29:36Z","title_canon_sha256":"4f6bbe77204ea512bba7a625de30aaea27b1dfa7dc375365c33f09ced3252eba"},"schema_version":"1.0","source":{"id":"1909.00734","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.00734","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"arxiv_version","alias_value":"1909.00734v1","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.00734","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"pith_short_12","alias_value":"DJTXDAFVMOPW","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"pith_short_16","alias_value":"DJTXDAFVMOPW3EX2","created_at":"2026-07-05T00:01:12Z"},{"alias_kind":"pith_short_8","alias_value":"DJTXDAFV","created_at":"2026-07-05T00:01:12Z"}],"graph_snapshots":[{"event_id":"sha256:af6342f5efb602cf3cd6354f65164a3a48a398ddd845f9e5b7307196c32cbd00","target":"graph","created_at":"2026-07-05T00:01:12Z","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/1909.00734/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems. Recent all-in-one style neural generation models have made impressive progress, yet they often produce outputs that are incoherent and unfaithful to the input. To address these issues, we present an end-to-end trained two-step generation model, where a sentence-level content planner first decides on the keyphrases to cover as well as a desired language style, followed by a surface realization decoder t","authors_text":"Lu Wang, Xinyu Hua","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-02T14:29:36Z","title":"Sentence-Level Content Planning and Style Specification for Neural Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.00734","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":"sha256:90c2ff3bd665d6bde1c788a2402f84fd61636b5e4f0ae0efe17824049d2a0d11","target":"record","created_at":"2026-07-05T00:01:12Z","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":"6ef6dd4e5b1bb7ce6ca028b872a332e8e416d68d06ff6e849f175909e9effd4d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-02T14:29:36Z","title_canon_sha256":"4f6bbe77204ea512bba7a625de30aaea27b1dfa7dc375365c33f09ced3252eba"},"schema_version":"1.0","source":{"id":"1909.00734","kind":"arxiv","version":1}},"canonical_sha256":"1a677180b5639f6d92fa4d2c1294b5213bf5c01f399c678968f88681638960ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a677180b5639f6d92fa4d2c1294b5213bf5c01f399c678968f88681638960ce","first_computed_at":"2026-07-05T00:01:12.633461Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:01:12.633461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/2qEOX18ahya9/Zk83CO0ILPMhppyNtB7xLlNPH0jCmfICjSLvfNYaF0WnGtE73DoS30n3OxlVq+uhZNqWAQDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:01:12.633845Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.00734","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90c2ff3bd665d6bde1c788a2402f84fd61636b5e4f0ae0efe17824049d2a0d11","sha256:af6342f5efb602cf3cd6354f65164a3a48a398ddd845f9e5b7307196c32cbd00"],"state_sha256":"2c625f1804655b806cf79d127af8a9e9fa7e873c35f1993285030fb5a48ca7c9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JoqEd0w0VoDc8BpgaqlPgA5SaenHGOMeAfa2TY9+qb/spmOeQjmF/i/0tba/eWz18KH/jPAd5+T9A7JUKLYFBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T17:18:09.483733Z","bundle_sha256":"4044cbed9df08e2c6c93bd9ebc470c56549bf73395a6a995fde528492f764aee"}}