{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:T4MOAHFLNMTBBZZD3TBHJARPBC","short_pith_number":"pith:T4MOAHFL","canonical_record":{"source":{"id":"1906.01081","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-03T21:13:07Z","cross_cats_sorted":[],"title_canon_sha256":"347510f1187cf1bf4bd72382818689307df03a0d309bf9c9a9eb82b0450118a5","abstract_canon_sha256":"b56d8b63b210a498349c92882097e9f0cfc7c4ac3b477b9a220e10f62c98e74a"},"schema_version":"1.0"},"canonical_sha256":"9f18e01cab6b2610e723dcc274822f08a32d2d9b7e7c17439d27ff42e8491010","source":{"kind":"arxiv","id":"1906.01081","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01081","created_at":"2026-05-17T23:44:17Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01081v1","created_at":"2026-05-17T23:44:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01081","created_at":"2026-05-17T23:44:17Z"},{"alias_kind":"pith_short_12","alias_value":"T4MOAHFLNMTB","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"T4MOAHFLNMTBBZZD","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"T4MOAHFL","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:T4MOAHFLNMTBBZZD3TBHJARPBC","target":"record","payload":{"canonical_record":{"source":{"id":"1906.01081","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-03T21:13:07Z","cross_cats_sorted":[],"title_canon_sha256":"347510f1187cf1bf4bd72382818689307df03a0d309bf9c9a9eb82b0450118a5","abstract_canon_sha256":"b56d8b63b210a498349c92882097e9f0cfc7c4ac3b477b9a220e10f62c98e74a"},"schema_version":"1.0"},"canonical_sha256":"9f18e01cab6b2610e723dcc274822f08a32d2d9b7e7c17439d27ff42e8491010","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:17.916560Z","signature_b64":"9Eix54GhWw9AWi2SEE3IlkcNwrcJ2lsZzBSf7i1qFDqKeG3gCr8qWjRMbqETUyPXMbBxheyNHYYGidjEKFG9Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f18e01cab6b2610e723dcc274822f08a32d2d9b7e7c17439d27ff42e8491010","last_reissued_at":"2026-05-17T23:44:17.916000Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:17.916000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.01081","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":"2026-05-17T23:44:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4lxK8oQKQgrtu3QFX7Au2q8rpC/Y14XDl99cWwuJXl20y/YVSM2zN7tzrTd0ar6TKeMiEelY7Zk6jdnt+EHiCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T12:20:05.450563Z"},"content_sha256":"7c7ce7bdabb4868a1df8667b00cdf8638db9a5f48da28b88d3714da51c896b86","schema_version":"1.0","event_id":"sha256:7c7ce7bdabb4868a1df8667b00cdf8638db9a5f48da28b88d3714da51c896b86"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:T4MOAHFLNMTBBZZD3TBHJARPBC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Handling Divergent Reference Texts when Evaluating Table-to-Text Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ankur Parikh, Bhuwan Dhingra, Dipanjan Das, Manaal Faruqui, Ming-Wei Chang, William W. Cohen","submitted_at":"2019-06-03T21:13:07Z","abstract_excerpt":"Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data. We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlation with human judgments when those references diverge. We propose a new metric, PARENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall. Through a large scale human evaluation study of table-to-t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01081","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":""},"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":"2026-05-17T23:44:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f+dpN4/iCScTRhbBeafl+wm77745pTWaYMtbH9ekZP6vk4DdOAh7238TK4F1pxl63Q6dN7Phhob2bbVveBtnAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T12:20:05.451251Z"},"content_sha256":"28bd5e3fd57d6e674ebe0f6f36166d6de83e30928e0b34ac22ab9e624661bacb","schema_version":"1.0","event_id":"sha256:28bd5e3fd57d6e674ebe0f6f36166d6de83e30928e0b34ac22ab9e624661bacb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T4MOAHFLNMTBBZZD3TBHJARPBC/bundle.json","state_url":"https://pith.science/pith/T4MOAHFLNMTBBZZD3TBHJARPBC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T4MOAHFLNMTBBZZD3TBHJARPBC/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":"2026-05-27T12:20:05Z","links":{"resolver":"https://pith.science/pith/T4MOAHFLNMTBBZZD3TBHJARPBC","bundle":"https://pith.science/pith/T4MOAHFLNMTBBZZD3TBHJARPBC/bundle.json","state":"https://pith.science/pith/T4MOAHFLNMTBBZZD3TBHJARPBC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T4MOAHFLNMTBBZZD3TBHJARPBC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:T4MOAHFLNMTBBZZD3TBHJARPBC","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":"b56d8b63b210a498349c92882097e9f0cfc7c4ac3b477b9a220e10f62c98e74a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-03T21:13:07Z","title_canon_sha256":"347510f1187cf1bf4bd72382818689307df03a0d309bf9c9a9eb82b0450118a5"},"schema_version":"1.0","source":{"id":"1906.01081","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01081","created_at":"2026-05-17T23:44:17Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01081v1","created_at":"2026-05-17T23:44:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01081","created_at":"2026-05-17T23:44:17Z"},{"alias_kind":"pith_short_12","alias_value":"T4MOAHFLNMTB","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"T4MOAHFLNMTBBZZD","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"T4MOAHFL","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:28bd5e3fd57d6e674ebe0f6f36166d6de83e30928e0b34ac22ab9e624661bacb","target":"graph","created_at":"2026-05-17T23:44:17Z","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"},"paper":{"abstract_excerpt":"Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data. We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlation with human judgments when those references diverge. We propose a new metric, PARENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall. Through a large scale human evaluation study of table-to-t","authors_text":"Ankur Parikh, Bhuwan Dhingra, Dipanjan Das, Manaal Faruqui, Ming-Wei Chang, William W. Cohen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-03T21:13:07Z","title":"Handling Divergent Reference Texts when Evaluating Table-to-Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01081","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":"sha256:7c7ce7bdabb4868a1df8667b00cdf8638db9a5f48da28b88d3714da51c896b86","target":"record","created_at":"2026-05-17T23:44:17Z","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":"b56d8b63b210a498349c92882097e9f0cfc7c4ac3b477b9a220e10f62c98e74a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-03T21:13:07Z","title_canon_sha256":"347510f1187cf1bf4bd72382818689307df03a0d309bf9c9a9eb82b0450118a5"},"schema_version":"1.0","source":{"id":"1906.01081","kind":"arxiv","version":1}},"canonical_sha256":"9f18e01cab6b2610e723dcc274822f08a32d2d9b7e7c17439d27ff42e8491010","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f18e01cab6b2610e723dcc274822f08a32d2d9b7e7c17439d27ff42e8491010","first_computed_at":"2026-05-17T23:44:17.916000Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:17.916000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9Eix54GhWw9AWi2SEE3IlkcNwrcJ2lsZzBSf7i1qFDqKeG3gCr8qWjRMbqETUyPXMbBxheyNHYYGidjEKFG9Dw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:17.916560Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.01081","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c7ce7bdabb4868a1df8667b00cdf8638db9a5f48da28b88d3714da51c896b86","sha256:28bd5e3fd57d6e674ebe0f6f36166d6de83e30928e0b34ac22ab9e624661bacb"],"state_sha256":"668e49837c4f23813a48e157065dd0937320ff7bd4afe3185ed3a932ac8bf08b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zuTppX0A5CCUWwtfQpIk12FITMJkEqTrf/SO9ph9hA3G4WXUlI+FyDIozfUazqu/ea+TaxAmpLLPQO1bhEacDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T12:20:05.454193Z","bundle_sha256":"e7a99ab497e360217017886705fcb69e351e9bfb67f65e91967ddf4f051172ac"}}