{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:L62V7HUYW2SCHDSRZELNIHITKH","short_pith_number":"pith:L62V7HUY","schema_version":"1.0","canonical_sha256":"5fb55f9e98b6a4238e51c916d41d1351e2d2fa148ef3fe2910cddcd9124c8709","source":{"kind":"arxiv","id":"1909.00325","version":1},"attestation_state":"computed","paper":{"title":"Repurposing Decoder-Transformer Language Models for Abstractive Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Alfredo L\\'ainez Rodrigo, Luke de Oliveira","submitted_at":"2019-09-01T05:26:30Z","abstract_excerpt":"Neural network models have shown excellent fluency and performance when applied to abstractive summarization. Many approaches to neural abstractive summarization involve the introduction of significant inductive bias, exemplified through the use of components such as pointer-generator architectures, coverage, and partially extractive procedures, designed to mimic the process by which humans summarize documents. We show that it is possible to attain competitive performance by instead directly viewing summarization as a language modeling problem and effectively leveraging transfer learning. We i"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1909.00325","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-01T05:26:30Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d4f658b9b56213b0db217826b9a29242ad1b867498865a28d8ea3f562df82a4c","abstract_canon_sha256":"5f262e3a9bc8e3eaab406874da70249d20e152045e1e3c1a367c94b6493602d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:01:07.968234Z","signature_b64":"1qOeXxO8OhM2OHuUTOiwjZ33cvn7LQSkq2xijyWoDFaCzZNvPBThEOqTFR3cCpZLhGMnQ+Wverw+ak97RNdyAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fb55f9e98b6a4238e51c916d41d1351e2d2fa148ef3fe2910cddcd9124c8709","last_reissued_at":"2026-07-05T00:01:07.967846Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:01:07.967846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Repurposing Decoder-Transformer Language Models for Abstractive Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Alfredo L\\'ainez Rodrigo, Luke de Oliveira","submitted_at":"2019-09-01T05:26:30Z","abstract_excerpt":"Neural network models have shown excellent fluency and performance when applied to abstractive summarization. Many approaches to neural abstractive summarization involve the introduction of significant inductive bias, exemplified through the use of components such as pointer-generator architectures, coverage, and partially extractive procedures, designed to mimic the process by which humans summarize documents. We show that it is possible to attain competitive performance by instead directly viewing summarization as a language modeling problem and effectively leveraging transfer learning. We i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.00325","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/1909.00325/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1909.00325","created_at":"2026-07-05T00:01:07.967902+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.00325v1","created_at":"2026-07-05T00:01:07.967902+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.00325","created_at":"2026-07-05T00:01:07.967902+00:00"},{"alias_kind":"pith_short_12","alias_value":"L62V7HUYW2SC","created_at":"2026-07-05T00:01:07.967902+00:00"},{"alias_kind":"pith_short_16","alias_value":"L62V7HUYW2SCHDSR","created_at":"2026-07-05T00:01:07.967902+00:00"},{"alias_kind":"pith_short_8","alias_value":"L62V7HUY","created_at":"2026-07-05T00:01:07.967902+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH","json":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH.json","graph_json":"https://pith.science/api/pith-number/L62V7HUYW2SCHDSRZELNIHITKH/graph.json","events_json":"https://pith.science/api/pith-number/L62V7HUYW2SCHDSRZELNIHITKH/events.json","paper":"https://pith.science/paper/L62V7HUY"},"agent_actions":{"view_html":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH","download_json":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH.json","view_paper":"https://pith.science/paper/L62V7HUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.00325&json=true","fetch_graph":"https://pith.science/api/pith-number/L62V7HUYW2SCHDSRZELNIHITKH/graph.json","fetch_events":"https://pith.science/api/pith-number/L62V7HUYW2SCHDSRZELNIHITKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH/action/storage_attestation","attest_author":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH/action/author_attestation","sign_citation":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH/action/citation_signature","submit_replication":"https://pith.science/pith/L62V7HUYW2SCHDSRZELNIHITKH/action/replication_record"}},"created_at":"2026-07-05T00:01:07.967902+00:00","updated_at":"2026-07-05T00:01:07.967902+00:00"}