{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TM7Q23B52T7VECA2CKLSVRKA7K","short_pith_number":"pith:TM7Q23B5","canonical_record":{"source":{"id":"1810.09995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-23T17:56:23Z","cross_cats_sorted":[],"title_canon_sha256":"50d6612657e13d5403a93b8aea59c7fbcdd7dea0f6efcf40147c0faf86ac6152","abstract_canon_sha256":"d9e114e585e5563a4dfd0ac894b08a81c0caff38d9068429c208dc32850daf31"},"schema_version":"1.0"},"canonical_sha256":"9b3f0d6c3dd4ff52081a12972ac540faa95675e48391b337d69a4a085b74507f","source":{"kind":"arxiv","id":"1810.09995","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.09995","created_at":"2026-05-18T00:02:28Z"},{"alias_kind":"arxiv_version","alias_value":"1810.09995v1","created_at":"2026-05-18T00:02:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09995","created_at":"2026-05-18T00:02:28Z"},{"alias_kind":"pith_short_12","alias_value":"TM7Q23B52T7V","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TM7Q23B52T7VECA2","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TM7Q23B5","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TM7Q23B52T7VECA2CKLSVRKA7K","target":"record","payload":{"canonical_record":{"source":{"id":"1810.09995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-23T17:56:23Z","cross_cats_sorted":[],"title_canon_sha256":"50d6612657e13d5403a93b8aea59c7fbcdd7dea0f6efcf40147c0faf86ac6152","abstract_canon_sha256":"d9e114e585e5563a4dfd0ac894b08a81c0caff38d9068429c208dc32850daf31"},"schema_version":"1.0"},"canonical_sha256":"9b3f0d6c3dd4ff52081a12972ac540faa95675e48391b337d69a4a085b74507f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:28.655460Z","signature_b64":"xf/hsIQZep66Rh1ZUMOia4wETNO5D5JCbnO7tQ4gtTDynXAr3fJDqwF99lA12GCslAxXf579IhXSjN2jTe0pAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b3f0d6c3dd4ff52081a12972ac540faa95675e48391b337d69a4a085b74507f","last_reissued_at":"2026-05-18T00:02:28.654828Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:28.654828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.09995","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":"2026-05-18T00:02:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XwnOTPjV6bBlIAAJBHCcUnyczye44DovndC8E4jJPyqrLp8gW1eS4Irn/pT3vVOzPPlO/t6Y12Woej6YdtLnBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T18:52:38.220854Z"},"content_sha256":"a799d51813a5f6c8c1990e289416cececc157abacc445b107807e3eb825e6fe6","schema_version":"1.0","event_id":"sha256:a799d51813a5f6c8c1990e289416cececc157abacc445b107807e3eb825e6fe6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TM7Q23B52T7VECA2CKLSVRKA7K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Graph Convolutional Encoders for Structured Data to Text Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Diego Marcheggiani, Laura Perez-Beltrachini","submitted_at":"2018-10-23T17:56:23Z","abstract_excerpt":"Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09995","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":"2026-05-18T00:02:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"omafuRfe/FvoE0JXyvDR/uohlmi5RrX+xb1bZRCEPvHsTQl4mxGv7ei7hZ2y00YJEJhC8wbPohaxJGTAaCzpDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T18:52:38.221222Z"},"content_sha256":"31b04b70516cd11edf9d37846ec85838a43acd9ef8a05646d8a627e9e6f4e3cf","schema_version":"1.0","event_id":"sha256:31b04b70516cd11edf9d37846ec85838a43acd9ef8a05646d8a627e9e6f4e3cf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TM7Q23B52T7VECA2CKLSVRKA7K/bundle.json","state_url":"https://pith.science/pith/TM7Q23B52T7VECA2CKLSVRKA7K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TM7Q23B52T7VECA2CKLSVRKA7K/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":"2026-06-27T18:52:38Z","links":{"resolver":"https://pith.science/pith/TM7Q23B52T7VECA2CKLSVRKA7K","bundle":"https://pith.science/pith/TM7Q23B52T7VECA2CKLSVRKA7K/bundle.json","state":"https://pith.science/pith/TM7Q23B52T7VECA2CKLSVRKA7K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TM7Q23B52T7VECA2CKLSVRKA7K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TM7Q23B52T7VECA2CKLSVRKA7K","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":"d9e114e585e5563a4dfd0ac894b08a81c0caff38d9068429c208dc32850daf31","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-23T17:56:23Z","title_canon_sha256":"50d6612657e13d5403a93b8aea59c7fbcdd7dea0f6efcf40147c0faf86ac6152"},"schema_version":"1.0","source":{"id":"1810.09995","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.09995","created_at":"2026-05-18T00:02:28Z"},{"alias_kind":"arxiv_version","alias_value":"1810.09995v1","created_at":"2026-05-18T00:02:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09995","created_at":"2026-05-18T00:02:28Z"},{"alias_kind":"pith_short_12","alias_value":"TM7Q23B52T7V","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TM7Q23B52T7VECA2","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TM7Q23B5","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:31b04b70516cd11edf9d37846ec85838a43acd9ef8a05646d8a627e9e6f4e3cf","target":"graph","created_at":"2026-05-18T00:02:28Z","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":"Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.","authors_text":"Diego Marcheggiani, Laura Perez-Beltrachini","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-23T17:56:23Z","title":"Deep Graph Convolutional Encoders for Structured Data to Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09995","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:a799d51813a5f6c8c1990e289416cececc157abacc445b107807e3eb825e6fe6","target":"record","created_at":"2026-05-18T00:02:28Z","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":"d9e114e585e5563a4dfd0ac894b08a81c0caff38d9068429c208dc32850daf31","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-23T17:56:23Z","title_canon_sha256":"50d6612657e13d5403a93b8aea59c7fbcdd7dea0f6efcf40147c0faf86ac6152"},"schema_version":"1.0","source":{"id":"1810.09995","kind":"arxiv","version":1}},"canonical_sha256":"9b3f0d6c3dd4ff52081a12972ac540faa95675e48391b337d69a4a085b74507f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b3f0d6c3dd4ff52081a12972ac540faa95675e48391b337d69a4a085b74507f","first_computed_at":"2026-05-18T00:02:28.654828Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:28.654828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xf/hsIQZep66Rh1ZUMOia4wETNO5D5JCbnO7tQ4gtTDynXAr3fJDqwF99lA12GCslAxXf579IhXSjN2jTe0pAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:28.655460Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.09995","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a799d51813a5f6c8c1990e289416cececc157abacc445b107807e3eb825e6fe6","sha256:31b04b70516cd11edf9d37846ec85838a43acd9ef8a05646d8a627e9e6f4e3cf"],"state_sha256":"26d3252fdc005254b34e3f83da83ef7470b27ea232ff7075621dd6f7b259d14b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OYGnCxEJwoMwemPEWHM7Kd2e4whm94mlhPGaTWD8Bp4opQjKCFZbeUvHkvAZQM0VvDfpqPFyCK3ULRd+V2ReCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-27T18:52:38.223486Z","bundle_sha256":"fb751aa52c56f4ee16a9e424a40524e2d311392e481281dab6e63d3ab9c0b900"}}