{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:LE5VXDBBMBFDU24IIKZ72ZADD5","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":"ea2a17e8b666bbaac486eb02dedfa4db5580f92a55b07212ee1748a7631b009a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-08T13:41:31Z","title_canon_sha256":"8aed1df68be376e1e0a5cc7ff372f2da6b07a7e6b11e99bd806066578c9fdba2"},"schema_version":"1.0","source":{"id":"1908.03067","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.03067","created_at":"2026-07-04T23:52:25Z"},{"alias_kind":"arxiv_version","alias_value":"1908.03067v1","created_at":"2026-07-04T23:52:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03067","created_at":"2026-07-04T23:52:25Z"},{"alias_kind":"pith_short_12","alias_value":"LE5VXDBBMBFD","created_at":"2026-07-04T23:52:25Z"},{"alias_kind":"pith_short_16","alias_value":"LE5VXDBBMBFDU24I","created_at":"2026-07-04T23:52:25Z"},{"alias_kind":"pith_short_8","alias_value":"LE5VXDBB","created_at":"2026-07-04T23:52:25Z"}],"graph_snapshots":[{"event_id":"sha256:ba802a8fd569aad0d8a027de22dce91b98855248744b8578c6922395a6b3e984","target":"graph","created_at":"2026-07-04T23:52:25Z","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/1908.03067/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Table-to-text generation aims to translate the structured data into the unstructured text. Most existing methods adopt the encoder-decoder framework to learn the transformation, which requires large-scale training samples. However, the lack of large parallel data is a major practical problem for many domains. In this work, we consider the scenario of low resource table-to-text generation, where only limited parallel data is available. We propose a novel model to separate the generation into two stages: key fact prediction and surface realization. It first predicts the key facts from the tables","authors_text":"Jie Zhou, Pengcheng Yang, Peng Li, Shuming Ma, Tianyu Liu, Xu Sun","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-08T13:41:31Z","title":"Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03067","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":"sha256:735fdb24c74ab1b1addb412597037aa8ddbfb7d0d2df7d625a92ef2e0b655fdb","target":"record","created_at":"2026-07-04T23:52:25Z","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":"ea2a17e8b666bbaac486eb02dedfa4db5580f92a55b07212ee1748a7631b009a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-08T13:41:31Z","title_canon_sha256":"8aed1df68be376e1e0a5cc7ff372f2da6b07a7e6b11e99bd806066578c9fdba2"},"schema_version":"1.0","source":{"id":"1908.03067","kind":"arxiv","version":1}},"canonical_sha256":"593b5b8c21604a3a6b8842b3fd64031f4c868b7f504a2c21bbc5bf7640f44709","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"593b5b8c21604a3a6b8842b3fd64031f4c868b7f504a2c21bbc5bf7640f44709","first_computed_at":"2026-07-04T23:52:25.907227Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:52:25.907227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wPdyW18bvwWW36AGhAtwd//+GvEaHjEcpoanNloTXJJ6nsYudzUaKyCSZTy/zdQ8W7K9jgMfBSPlwB/Je6G0AQ==","signature_status":"signed_v1","signed_at":"2026-07-04T23:52:25.907731Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.03067","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:735fdb24c74ab1b1addb412597037aa8ddbfb7d0d2df7d625a92ef2e0b655fdb","sha256:ba802a8fd569aad0d8a027de22dce91b98855248744b8578c6922395a6b3e984"],"state_sha256":"b33c99bdab55fdff8bb7b8315d9ae7ef43844d739948feb6e1a22a7954364df9"}