{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:N3C3MMDQEU45HGXQNTTLK7IZKR","short_pith_number":"pith:N3C3MMDQ","canonical_record":{"source":{"id":"1711.02608","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-07T17:01:55Z","cross_cats_sorted":[],"title_canon_sha256":"e5cd3f6a94285c50c9254fd0db90fa428030163f3789cebf3ab22ce69c1e051d","abstract_canon_sha256":"73d2e35e5386e61b2132fb9422eb37758bd359dd78f37de897fda8961956ec68"},"schema_version":"1.0"},"canonical_sha256":"6ec5b630702539d39af06ce6b57d19544d5418364770af38611bf8e5e8376b8e","source":{"kind":"arxiv","id":"1711.02608","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.02608","created_at":"2026-05-18T00:20:21Z"},{"alias_kind":"arxiv_version","alias_value":"1711.02608v1","created_at":"2026-05-18T00:20:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.02608","created_at":"2026-05-18T00:20:21Z"},{"alias_kind":"pith_short_12","alias_value":"N3C3MMDQEU45","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"N3C3MMDQEU45HGXQ","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"N3C3MMDQ","created_at":"2026-05-18T12:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:N3C3MMDQEU45HGXQNTTLK7IZKR","target":"record","payload":{"canonical_record":{"source":{"id":"1711.02608","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-07T17:01:55Z","cross_cats_sorted":[],"title_canon_sha256":"e5cd3f6a94285c50c9254fd0db90fa428030163f3789cebf3ab22ce69c1e051d","abstract_canon_sha256":"73d2e35e5386e61b2132fb9422eb37758bd359dd78f37de897fda8961956ec68"},"schema_version":"1.0"},"canonical_sha256":"6ec5b630702539d39af06ce6b57d19544d5418364770af38611bf8e5e8376b8e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:21.935902Z","signature_b64":"uevTx1scJiBb1plqeSdp9fkMRL2LfafHhfKKkgR4Z9lmz17jOuhHM8XP0ubZ1phkKwgAJMwHJv0wJNWTN434AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ec5b630702539d39af06ce6b57d19544d5418364770af38611bf8e5e8376b8e","last_reissued_at":"2026-05-18T00:20:21.935339Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:21.935339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.02608","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":"2026-05-18T00:20:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T6P44VWclnefsVWONEF0SjJp3TEI2xcYAvocgEzQNslHXsP2yGxf3YU4vURpLNDAHHrzEuVQSjVLtlS7gsd3BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T04:07:56.283885Z"},"content_sha256":"fa950156aa370bf655eb0c0b4fb5967b1144f57701fdb7ddb72b788d05b8685a","schema_version":"1.0","event_id":"sha256:fa950156aa370bf655eb0c0b4fb5967b1144f57701fdb7ddb72b788d05b8685a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:N3C3MMDQEU45HGXQNTTLK7IZKR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Extractive Multi-document Summarization Using Multilayer Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Diego R. Amancio, Jorge V. Tohalino","submitted_at":"2017-11-07T17:01:55Z","abstract_excerpt":"Huge volumes of textual information has been produced every single day. In order to organize and understand such large datasets, in recent years, summarization techniques have become popular. These techniques aims at finding relevant, concise and non-redundant content from such a big data. While network methods have been adopted to model texts in some scenarios, a systematic evaluation of multilayer network models in the multi-document summarization task has been limited to a few studies. Here, we evaluate the performance of a multilayer-based method to select the most relevant sentences in th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.02608","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":"2026-05-18T00:20:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eJxu8TRyAz5uJa22xOQjRnNlj3e36LCYkAtkkOT6ZXNxCPYWKOPH4O0hZePLIJo/romsmz0XEX2lDx+LCUifBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T04:07:56.284684Z"},"content_sha256":"3bc2ebc6db1dc4b73bacb13312991f6c10d3a0ebb5b7e1ba4ab411dc50c6df36","schema_version":"1.0","event_id":"sha256:3bc2ebc6db1dc4b73bacb13312991f6c10d3a0ebb5b7e1ba4ab411dc50c6df36"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N3C3MMDQEU45HGXQNTTLK7IZKR/bundle.json","state_url":"https://pith.science/pith/N3C3MMDQEU45HGXQNTTLK7IZKR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N3C3MMDQEU45HGXQNTTLK7IZKR/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":"2026-05-26T04:07:56Z","links":{"resolver":"https://pith.science/pith/N3C3MMDQEU45HGXQNTTLK7IZKR","bundle":"https://pith.science/pith/N3C3MMDQEU45HGXQNTTLK7IZKR/bundle.json","state":"https://pith.science/pith/N3C3MMDQEU45HGXQNTTLK7IZKR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N3C3MMDQEU45HGXQNTTLK7IZKR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:N3C3MMDQEU45HGXQNTTLK7IZKR","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":"73d2e35e5386e61b2132fb9422eb37758bd359dd78f37de897fda8961956ec68","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-07T17:01:55Z","title_canon_sha256":"e5cd3f6a94285c50c9254fd0db90fa428030163f3789cebf3ab22ce69c1e051d"},"schema_version":"1.0","source":{"id":"1711.02608","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.02608","created_at":"2026-05-18T00:20:21Z"},{"alias_kind":"arxiv_version","alias_value":"1711.02608v1","created_at":"2026-05-18T00:20:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.02608","created_at":"2026-05-18T00:20:21Z"},{"alias_kind":"pith_short_12","alias_value":"N3C3MMDQEU45","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"N3C3MMDQEU45HGXQ","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"N3C3MMDQ","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:3bc2ebc6db1dc4b73bacb13312991f6c10d3a0ebb5b7e1ba4ab411dc50c6df36","target":"graph","created_at":"2026-05-18T00:20:21Z","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":"Huge volumes of textual information has been produced every single day. In order to organize and understand such large datasets, in recent years, summarization techniques have become popular. These techniques aims at finding relevant, concise and non-redundant content from such a big data. While network methods have been adopted to model texts in some scenarios, a systematic evaluation of multilayer network models in the multi-document summarization task has been limited to a few studies. Here, we evaluate the performance of a multilayer-based method to select the most relevant sentences in th","authors_text":"Diego R. Amancio, Jorge V. Tohalino","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-07T17:01:55Z","title":"Extractive Multi-document Summarization Using Multilayer Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.02608","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:fa950156aa370bf655eb0c0b4fb5967b1144f57701fdb7ddb72b788d05b8685a","target":"record","created_at":"2026-05-18T00:20:21Z","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":"73d2e35e5386e61b2132fb9422eb37758bd359dd78f37de897fda8961956ec68","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-11-07T17:01:55Z","title_canon_sha256":"e5cd3f6a94285c50c9254fd0db90fa428030163f3789cebf3ab22ce69c1e051d"},"schema_version":"1.0","source":{"id":"1711.02608","kind":"arxiv","version":1}},"canonical_sha256":"6ec5b630702539d39af06ce6b57d19544d5418364770af38611bf8e5e8376b8e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6ec5b630702539d39af06ce6b57d19544d5418364770af38611bf8e5e8376b8e","first_computed_at":"2026-05-18T00:20:21.935339Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:20:21.935339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uevTx1scJiBb1plqeSdp9fkMRL2LfafHhfKKkgR4Z9lmz17jOuhHM8XP0ubZ1phkKwgAJMwHJv0wJNWTN434AA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:20:21.935902Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.02608","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fa950156aa370bf655eb0c0b4fb5967b1144f57701fdb7ddb72b788d05b8685a","sha256:3bc2ebc6db1dc4b73bacb13312991f6c10d3a0ebb5b7e1ba4ab411dc50c6df36"],"state_sha256":"6a1ef34e7671977f8f9b4315d35188d9be04a2a7aa3d48b91d0e11249db4274c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uGgzQDYyGDmmO1ArPAbdrXf+H8xTbxnuXtlLxDIykeEupBU8eLd7PO95zVieuHwflti00RUewkzj16+U8OPaAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T04:07:56.288798Z","bundle_sha256":"9e30583bc3ebd6b1731b001c028ebc89a968702d5da088e9c989965c9fe84d5f"}}