{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:6ZFHXLHX5C56WB5ZZL6WMLZFYQ","short_pith_number":"pith:6ZFHXLHX","canonical_record":{"source":{"id":"2106.11520","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-22T03:20:53Z","cross_cats_sorted":[],"title_canon_sha256":"4e40fbdf28c23dedc70b5902861e6ad7536ebdb366428ded5c8ab1ce7069c153","abstract_canon_sha256":"849afc6405342168773c0c1100e01cec53ae4b8a7990bf4456f0bcd24c437a56"},"schema_version":"1.0"},"canonical_sha256":"f64a7bacf7e8bbeb07b9cafd662f25c439a65ab85d451a8fdd0468ef2bc14b15","source":{"kind":"arxiv","id":"2106.11520","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.11520","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"arxiv_version","alias_value":"2106.11520v2","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.11520","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_12","alias_value":"6ZFHXLHX5C56","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_16","alias_value":"6ZFHXLHX5C56WB5Z","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_8","alias_value":"6ZFHXLHX","created_at":"2026-07-05T03:26:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:6ZFHXLHX5C56WB5ZZL6WMLZFYQ","target":"record","payload":{"canonical_record":{"source":{"id":"2106.11520","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-22T03:20:53Z","cross_cats_sorted":[],"title_canon_sha256":"4e40fbdf28c23dedc70b5902861e6ad7536ebdb366428ded5c8ab1ce7069c153","abstract_canon_sha256":"849afc6405342168773c0c1100e01cec53ae4b8a7990bf4456f0bcd24c437a56"},"schema_version":"1.0"},"canonical_sha256":"f64a7bacf7e8bbeb07b9cafd662f25c439a65ab85d451a8fdd0468ef2bc14b15","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:26:18.719074Z","signature_b64":"ht5+ItLfi6MoEnLkocs4qnr7q6VOhlwAuDZ6pMFzeG3vlpI0/cTyvLTS47HxVSaWtdbXKTUzq2ok9rAVumWqBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f64a7bacf7e8bbeb07b9cafd662f25c439a65ab85d451a8fdd0468ef2bc14b15","last_reissued_at":"2026-07-05T03:26:18.718644Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:26:18.718644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.11520","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:26:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GqmYpersgPcCJQtRZJa785FmkeYdQOEWLCWqUXRcrhpJuFc6Wk/FF/+pf1Z/PETFBK8dOWuFFT9JScQFG/x0Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T13:00:09.113977Z"},"content_sha256":"34c753d69a434b4cd7393506779afed5de43aa7714de17b60e36ff8fdd3af239","schema_version":"1.0","event_id":"sha256:34c753d69a434b4cd7393506779afed5de43aa7714de17b60e36ff8fdd3af239"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:6ZFHXLHX5C56WB5ZZL6WMLZFYQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BARTScore: Evaluating Generated Text as Text Generation","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Graham Neubig, Pengfei Liu, Weizhe Yuan","submitted_at":"2021-06-22T03:20:53Z","abstract_excerpt":"A wide variety of NLP applications, such as machine translation, summarization, and dialog, involve text generation. One major challenge for these applications is how to evaluate whether such generated texts are actually fluent, accurate, or effective. In this work, we conceptualize the evaluation of generated text as a text generation problem, modeled using pre-trained sequence-to-sequence models. The general idea is that models trained to convert the generated text to/from a reference output or the source text will achieve higher scores when the generated text is better. We operationalize th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.11520","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/2106.11520/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":"2026-07-05T03:26:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LidRKEJHDxVqYt0wqAodmJtcepy3Eyj6cC4rWBiKln3UCtRIqkKivyaX+GuwC7lLUqVqFZ8UZgmy2YPFy8dnBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T13:00:09.114926Z"},"content_sha256":"1e67c110ec52f146648ebf0da39fe8a861b74bfc05a5da392aa702ea3146e68a","schema_version":"1.0","event_id":"sha256:1e67c110ec52f146648ebf0da39fe8a861b74bfc05a5da392aa702ea3146e68a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6ZFHXLHX5C56WB5ZZL6WMLZFYQ/bundle.json","state_url":"https://pith.science/pith/6ZFHXLHX5C56WB5ZZL6WMLZFYQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6ZFHXLHX5C56WB5ZZL6WMLZFYQ/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":"2026-08-17T13:00:09Z","links":{"resolver":"https://pith.science/pith/6ZFHXLHX5C56WB5ZZL6WMLZFYQ","bundle":"https://pith.science/pith/6ZFHXLHX5C56WB5ZZL6WMLZFYQ/bundle.json","state":"https://pith.science/pith/6ZFHXLHX5C56WB5ZZL6WMLZFYQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6ZFHXLHX5C56WB5ZZL6WMLZFYQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:6ZFHXLHX5C56WB5ZZL6WMLZFYQ","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":"849afc6405342168773c0c1100e01cec53ae4b8a7990bf4456f0bcd24c437a56","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-22T03:20:53Z","title_canon_sha256":"4e40fbdf28c23dedc70b5902861e6ad7536ebdb366428ded5c8ab1ce7069c153"},"schema_version":"1.0","source":{"id":"2106.11520","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.11520","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"arxiv_version","alias_value":"2106.11520v2","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.11520","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_12","alias_value":"6ZFHXLHX5C56","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_16","alias_value":"6ZFHXLHX5C56WB5Z","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_8","alias_value":"6ZFHXLHX","created_at":"2026-07-05T03:26:18Z"}],"graph_snapshots":[{"event_id":"sha256:1e67c110ec52f146648ebf0da39fe8a861b74bfc05a5da392aa702ea3146e68a","target":"graph","created_at":"2026-07-05T03:26:18Z","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/2106.11520/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A wide variety of NLP applications, such as machine translation, summarization, and dialog, involve text generation. One major challenge for these applications is how to evaluate whether such generated texts are actually fluent, accurate, or effective. In this work, we conceptualize the evaluation of generated text as a text generation problem, modeled using pre-trained sequence-to-sequence models. The general idea is that models trained to convert the generated text to/from a reference output or the source text will achieve higher scores when the generated text is better. We operationalize th","authors_text":"Graham Neubig, Pengfei Liu, Weizhe Yuan","cross_cats":[],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-22T03:20:53Z","title":"BARTScore: Evaluating Generated Text as Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.11520","kind":"arxiv","version":2},"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":"sha256:34c753d69a434b4cd7393506779afed5de43aa7714de17b60e36ff8fdd3af239","target":"record","created_at":"2026-07-05T03:26:18Z","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":"849afc6405342168773c0c1100e01cec53ae4b8a7990bf4456f0bcd24c437a56","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-22T03:20:53Z","title_canon_sha256":"4e40fbdf28c23dedc70b5902861e6ad7536ebdb366428ded5c8ab1ce7069c153"},"schema_version":"1.0","source":{"id":"2106.11520","kind":"arxiv","version":2}},"canonical_sha256":"f64a7bacf7e8bbeb07b9cafd662f25c439a65ab85d451a8fdd0468ef2bc14b15","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f64a7bacf7e8bbeb07b9cafd662f25c439a65ab85d451a8fdd0468ef2bc14b15","first_computed_at":"2026-07-05T03:26:18.718644Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:26:18.718644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ht5+ItLfi6MoEnLkocs4qnr7q6VOhlwAuDZ6pMFzeG3vlpI0/cTyvLTS47HxVSaWtdbXKTUzq2ok9rAVumWqBw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:26:18.719074Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.11520","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34c753d69a434b4cd7393506779afed5de43aa7714de17b60e36ff8fdd3af239","sha256:1e67c110ec52f146648ebf0da39fe8a861b74bfc05a5da392aa702ea3146e68a"],"state_sha256":"f39d123b169544cd7dae1379235c6d57173bf5bd9fe4e8bf0bbb2b13f9ed2189"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jquxHGXSkTY1xujERzVRSw2CXzMD4xh2GNge0CUkHZTK8WYS4+RkbAwFX+ILm2ruk5JWICXDxEqnusZLjcL7Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T13:00:09.122945Z","bundle_sha256":"63e7701fde07a099850bb0906e73f440546b23f83408bf35646de52f06a9eddc"}}