{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:QAFINPZT5VPJTBE5K7Q2SSD3K3","short_pith_number":"pith:QAFINPZT","canonical_record":{"source":{"id":"2110.11870","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T17:13:56Z","cross_cats_sorted":[],"title_canon_sha256":"e148c5de6f5049d2e9c99d8f2439733944b4a42733fbbb2ad712c3a93f4b404f","abstract_canon_sha256":"1499443b3ad8fc4861471e1dd8838f1b416e163e5583804a623f44b44a69a828"},"schema_version":"1.0"},"canonical_sha256":"800a86bf33ed5e99849d57e1a9487b56cc7154a258b734069da2911014dd7851","source":{"kind":"arxiv","id":"2110.11870","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.11870","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"arxiv_version","alias_value":"2110.11870v1","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.11870","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"pith_short_12","alias_value":"QAFINPZT5VPJ","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"pith_short_16","alias_value":"QAFINPZT5VPJTBE5","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"pith_short_8","alias_value":"QAFINPZT","created_at":"2026-07-05T03:24:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:QAFINPZT5VPJTBE5K7Q2SSD3K3","target":"record","payload":{"canonical_record":{"source":{"id":"2110.11870","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T17:13:56Z","cross_cats_sorted":[],"title_canon_sha256":"e148c5de6f5049d2e9c99d8f2439733944b4a42733fbbb2ad712c3a93f4b404f","abstract_canon_sha256":"1499443b3ad8fc4861471e1dd8838f1b416e163e5583804a623f44b44a69a828"},"schema_version":"1.0"},"canonical_sha256":"800a86bf33ed5e99849d57e1a9487b56cc7154a258b734069da2911014dd7851","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:24:52.472112Z","signature_b64":"0hAolSVi5gjN6KRdl1QW9dd9IMwFfd1RTMu9YBVP45LhLSFnwXqo/TCHA5BmGIWJpqUT3B2Q29db5/Ol8bCGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"800a86bf33ed5e99849d57e1a9487b56cc7154a258b734069da2911014dd7851","last_reissued_at":"2026-07-05T03:24:52.471721Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:24:52.471721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.11870","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":"2026-07-05T03:24:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GZ5LB6IipvjZVT7O/lW6fVDxYJmUlXwC++T5zQ+LEJ5gXwUooIKyw/FqosqjMBF8v21es4EO4YArDUT+xxwnCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:41:18.517974Z"},"content_sha256":"3654bed63b162b288b9ced400698f39e3157064115ca56cde0011ffba4daee0f","schema_version":"1.0","event_id":"sha256:3654bed63b162b288b9ced400698f39e3157064115ca56cde0011ffba4daee0f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:QAFINPZT5VPJTBE5K7Q2SSD3K3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Biomedical text summarization using Conditional Generative Adversarial Network(CGAN)","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Abdolreza Mirzaei, Mehran Safayani, Seyed Vahid Moravvej","submitted_at":"2021-09-17T17:13:56Z","abstract_excerpt":"Text summarization in medicine can help doctors for reducing the time to access important information from countless documents. The paper offers a supervised extractive summarization method based on conditional generative adversarial networks using convolutional neural networks. Unlike previous models, which often use greedy methods to select sentences, we use a new approach for selecting sentences. Moreover, we provide a network for biomedical word embedding, which improves summarization. An essential contribution of the paper is introducing a new loss function for the discriminator, making t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.11870","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2110.11870/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":"2026-07-05T03:24:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j1bqj1I2YoOatrA1qFbLjWYBSni6e6tsfFs2oHQl1hq5+/x4SSTol1JlSLoXqu8BTO52DZvgt+5YFsXobsW5DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:41:18.518478Z"},"content_sha256":"082888432b4701fc99cb24f62d7a5b21006b6de01fcbd410db828fb743aeb2e7","schema_version":"1.0","event_id":"sha256:082888432b4701fc99cb24f62d7a5b21006b6de01fcbd410db828fb743aeb2e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QAFINPZT5VPJTBE5K7Q2SSD3K3/bundle.json","state_url":"https://pith.science/pith/QAFINPZT5VPJTBE5K7Q2SSD3K3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QAFINPZT5VPJTBE5K7Q2SSD3K3/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":"2026-08-21T15:41:18Z","links":{"resolver":"https://pith.science/pith/QAFINPZT5VPJTBE5K7Q2SSD3K3","bundle":"https://pith.science/pith/QAFINPZT5VPJTBE5K7Q2SSD3K3/bundle.json","state":"https://pith.science/pith/QAFINPZT5VPJTBE5K7Q2SSD3K3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QAFINPZT5VPJTBE5K7Q2SSD3K3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:QAFINPZT5VPJTBE5K7Q2SSD3K3","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":"1499443b3ad8fc4861471e1dd8838f1b416e163e5583804a623f44b44a69a828","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T17:13:56Z","title_canon_sha256":"e148c5de6f5049d2e9c99d8f2439733944b4a42733fbbb2ad712c3a93f4b404f"},"schema_version":"1.0","source":{"id":"2110.11870","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.11870","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"arxiv_version","alias_value":"2110.11870v1","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.11870","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"pith_short_12","alias_value":"QAFINPZT5VPJ","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"pith_short_16","alias_value":"QAFINPZT5VPJTBE5","created_at":"2026-07-05T03:24:52Z"},{"alias_kind":"pith_short_8","alias_value":"QAFINPZT","created_at":"2026-07-05T03:24:52Z"}],"graph_snapshots":[{"event_id":"sha256:082888432b4701fc99cb24f62d7a5b21006b6de01fcbd410db828fb743aeb2e7","target":"graph","created_at":"2026-07-05T03:24:52Z","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/2110.11870/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text summarization in medicine can help doctors for reducing the time to access important information from countless documents. The paper offers a supervised extractive summarization method based on conditional generative adversarial networks using convolutional neural networks. Unlike previous models, which often use greedy methods to select sentences, we use a new approach for selecting sentences. Moreover, we provide a network for biomedical word embedding, which improves summarization. An essential contribution of the paper is introducing a new loss function for the discriminator, making t","authors_text":"Abdolreza Mirzaei, Mehran Safayani, Seyed Vahid Moravvej","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T17:13:56Z","title":"Biomedical text summarization using Conditional Generative Adversarial Network(CGAN)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.11870","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":"sha256:3654bed63b162b288b9ced400698f39e3157064115ca56cde0011ffba4daee0f","target":"record","created_at":"2026-07-05T03:24:52Z","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":"1499443b3ad8fc4861471e1dd8838f1b416e163e5583804a623f44b44a69a828","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T17:13:56Z","title_canon_sha256":"e148c5de6f5049d2e9c99d8f2439733944b4a42733fbbb2ad712c3a93f4b404f"},"schema_version":"1.0","source":{"id":"2110.11870","kind":"arxiv","version":1}},"canonical_sha256":"800a86bf33ed5e99849d57e1a9487b56cc7154a258b734069da2911014dd7851","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"800a86bf33ed5e99849d57e1a9487b56cc7154a258b734069da2911014dd7851","first_computed_at":"2026-07-05T03:24:52.471721Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:24:52.471721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0hAolSVi5gjN6KRdl1QW9dd9IMwFfd1RTMu9YBVP45LhLSFnwXqo/TCHA5BmGIWJpqUT3B2Q29db5/Ol8bCGBg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:24:52.472112Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.11870","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3654bed63b162b288b9ced400698f39e3157064115ca56cde0011ffba4daee0f","sha256:082888432b4701fc99cb24f62d7a5b21006b6de01fcbd410db828fb743aeb2e7"],"state_sha256":"16f42aec1ae056d4ac20acd957ac9a1a2c39006b4123c92cf7dc8761fe50e343"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nd9G54imVIM25S9yGnIbfrJODXiKA9BgFRMXxEi3ESbUJN7u03YVp7ljAM9CralZgU3hNmGCRtXw7fGlDoguBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T15:41:18.522854Z","bundle_sha256":"14bb0071123b1be5030cf32bbbef999354f7ab2cee7f568b406079d23be51f4c"}}