{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HZKSSXG3O2QEERZLTPPPIZ5WGV","short_pith_number":"pith:HZKSSXG3","canonical_record":{"source":{"id":"1811.01824","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-05T16:12:04Z","cross_cats_sorted":["cs.CL","cs.SE","stat.ML"],"title_canon_sha256":"0c9d26ddf501ebe0638f2f45e1ce4a2c67698b808273a1a9d7cc5492c7ec1e4c","abstract_canon_sha256":"c425e0193af8b4f7e0ce36e74751c64d5ce3437b758f92edfaad0eaeb2b2843a"},"schema_version":"1.0"},"canonical_sha256":"3e55295cdb76a042472b9bdef467b6355805b0bd9023c3e327f9608adca142bf","source":{"kind":"arxiv","id":"1811.01824","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.01824","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"arxiv_version","alias_value":"1811.01824v4","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01824","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"pith_short_12","alias_value":"HZKSSXG3O2QE","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"pith_short_16","alias_value":"HZKSSXG3O2QEERZL","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"pith_short_8","alias_value":"HZKSSXG3","created_at":"2026-07-05T02:12:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HZKSSXG3O2QEERZLTPPPIZ5WGV","target":"record","payload":{"canonical_record":{"source":{"id":"1811.01824","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-05T16:12:04Z","cross_cats_sorted":["cs.CL","cs.SE","stat.ML"],"title_canon_sha256":"0c9d26ddf501ebe0638f2f45e1ce4a2c67698b808273a1a9d7cc5492c7ec1e4c","abstract_canon_sha256":"c425e0193af8b4f7e0ce36e74751c64d5ce3437b758f92edfaad0eaeb2b2843a"},"schema_version":"1.0"},"canonical_sha256":"3e55295cdb76a042472b9bdef467b6355805b0bd9023c3e327f9608adca142bf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:12:27.759128Z","signature_b64":"TYyNEwkTsmIg1/KRLgjTXlpXBaBO2iHTltJDAuyPHmOXRqTT/VK1jqEmFwVgudpj3/sGIVs5k9x87Fm83cs6Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e55295cdb76a042472b9bdef467b6355805b0bd9023c3e327f9608adca142bf","last_reissued_at":"2026-07-05T02:12:27.758332Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:12:27.758332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.01824","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:12:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aXVPlqDpJoyEKMegWXi3c0bzpX2I2pXCd6gSbyllmsqU3+k/Mit3NqfrbYzmeViIQrs2JIr1q9cGqfB43IWqDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:46:49.411858Z"},"content_sha256":"db7d78d1615adce3f1bb0ff2cbd30d2a4bb88cb64d23002c6c3ec9abc82e5376","schema_version":"1.0","event_id":"sha256:db7d78d1615adce3f1bb0ff2cbd30d2a4bb88cb64d23002c6c3ec9abc82e5376"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HZKSSXG3O2QEERZLTPPPIZ5WGV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Structured Neural Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Marc Brockschmidt, Miltiadis Allamanis, Patrick Fernandes","submitted_at":"2018-11-05T16:12:04Z","abstract_excerpt":"Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01824","kind":"arxiv","version":4},"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/1811.01824/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":"2026-07-05T02:12:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S1sXi3SPBOqGoFwdP2ym/63zdQ0leoXMu6g8quoBhfEuL7pjsiap+Cr26k8snqwz2iC75syXwmT18dISNrAzBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:46:49.412802Z"},"content_sha256":"4c8585658be09d923b600fa40077987a4516480dc1803190e2deee56a79f9cb0","schema_version":"1.0","event_id":"sha256:4c8585658be09d923b600fa40077987a4516480dc1803190e2deee56a79f9cb0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HZKSSXG3O2QEERZLTPPPIZ5WGV/bundle.json","state_url":"https://pith.science/pith/HZKSSXG3O2QEERZLTPPPIZ5WGV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HZKSSXG3O2QEERZLTPPPIZ5WGV/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":"2026-08-05T15:46:49Z","links":{"resolver":"https://pith.science/pith/HZKSSXG3O2QEERZLTPPPIZ5WGV","bundle":"https://pith.science/pith/HZKSSXG3O2QEERZLTPPPIZ5WGV/bundle.json","state":"https://pith.science/pith/HZKSSXG3O2QEERZLTPPPIZ5WGV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HZKSSXG3O2QEERZLTPPPIZ5WGV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HZKSSXG3O2QEERZLTPPPIZ5WGV","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":"c425e0193af8b4f7e0ce36e74751c64d5ce3437b758f92edfaad0eaeb2b2843a","cross_cats_sorted":["cs.CL","cs.SE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-05T16:12:04Z","title_canon_sha256":"0c9d26ddf501ebe0638f2f45e1ce4a2c67698b808273a1a9d7cc5492c7ec1e4c"},"schema_version":"1.0","source":{"id":"1811.01824","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.01824","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"arxiv_version","alias_value":"1811.01824v4","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01824","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"pith_short_12","alias_value":"HZKSSXG3O2QE","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"pith_short_16","alias_value":"HZKSSXG3O2QEERZL","created_at":"2026-07-05T02:12:27Z"},{"alias_kind":"pith_short_8","alias_value":"HZKSSXG3","created_at":"2026-07-05T02:12:27Z"}],"graph_snapshots":[{"event_id":"sha256:4c8585658be09d923b600fa40077987a4516480dc1803190e2deee56a79f9cb0","target":"graph","created_at":"2026-07-05T02:12:27Z","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/1811.01824/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.","authors_text":"Marc Brockschmidt, Miltiadis Allamanis, Patrick Fernandes","cross_cats":["cs.CL","cs.SE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-05T16:12:04Z","title":"Structured Neural Summarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01824","kind":"arxiv","version":4},"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:db7d78d1615adce3f1bb0ff2cbd30d2a4bb88cb64d23002c6c3ec9abc82e5376","target":"record","created_at":"2026-07-05T02:12:27Z","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":"c425e0193af8b4f7e0ce36e74751c64d5ce3437b758f92edfaad0eaeb2b2843a","cross_cats_sorted":["cs.CL","cs.SE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-05T16:12:04Z","title_canon_sha256":"0c9d26ddf501ebe0638f2f45e1ce4a2c67698b808273a1a9d7cc5492c7ec1e4c"},"schema_version":"1.0","source":{"id":"1811.01824","kind":"arxiv","version":4}},"canonical_sha256":"3e55295cdb76a042472b9bdef467b6355805b0bd9023c3e327f9608adca142bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3e55295cdb76a042472b9bdef467b6355805b0bd9023c3e327f9608adca142bf","first_computed_at":"2026-07-05T02:12:27.758332Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:12:27.758332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TYyNEwkTsmIg1/KRLgjTXlpXBaBO2iHTltJDAuyPHmOXRqTT/VK1jqEmFwVgudpj3/sGIVs5k9x87Fm83cs6Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:12:27.759128Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.01824","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db7d78d1615adce3f1bb0ff2cbd30d2a4bb88cb64d23002c6c3ec9abc82e5376","sha256:4c8585658be09d923b600fa40077987a4516480dc1803190e2deee56a79f9cb0"],"state_sha256":"0a24d9fb4cf833f96524a36cf9accbe1d36f2ee525bc7529eefc0e79749c3127"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T9YAAMgjAJ4xg4EyVTf1195mS9YfSVwOjub9M/hSfzuYgUmwynlKOfygKa/u20GXYdvI2DWFQ4jbUbOLiaoiDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T15:46:49.426702Z","bundle_sha256":"f1b673b538a0d1fc34608c80cb89a7ebc6f04efbc36a62622e34d76586229b86"}}