{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:MNNPJ7FU3QW3CB4LMTCHIXN5N2","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":"3f8bd999e35a71133072d0d5f1f6f2285aaeb7e6522fac474641146b44b9f61a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-23T20:10:56Z","title_canon_sha256":"ae947d946cef197b2f76418660e2d4ba47f23b2c80befb186f28584af20f1da9"},"schema_version":"1.0","source":{"id":"1909.10599","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.10599","created_at":"2026-07-05T00:06:22Z"},{"alias_kind":"arxiv_version","alias_value":"1909.10599v1","created_at":"2026-07-05T00:06:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.10599","created_at":"2026-07-05T00:06:22Z"},{"alias_kind":"pith_short_12","alias_value":"MNNPJ7FU3QW3","created_at":"2026-07-05T00:06:22Z"},{"alias_kind":"pith_short_16","alias_value":"MNNPJ7FU3QW3CB4L","created_at":"2026-07-05T00:06:22Z"},{"alias_kind":"pith_short_8","alias_value":"MNNPJ7FU","created_at":"2026-07-05T00:06:22Z"}],"graph_snapshots":[{"event_id":"sha256:5b3054f693b92300a93d5e3a9d08068e73eeeea656a732e26bf1dd574e2969d4","target":"graph","created_at":"2026-07-05T00:06:22Z","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/1909.10599/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural models for abstractive summarization tend to achieve the best performance in the presence of highly specialized, summarization specific modeling add-ons such as pointer-generator, coverage-modeling, and inferencetime heuristics. We show here that pretraining can complement such modeling advancements to yield improved results in both short-form and long-form abstractive summarization using two key concepts: full-network initialization and multi-stage pretraining. Our method allows the model to transitively benefit from multiple pretraining tasks, from generic language tasks to a speciali","authors_text":"Radu Soricut, Sebastian Goodman, Zhenzhong Lan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-23T20:10:56Z","title":"Multi-stage Pretraining for Abstractive Summarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.10599","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:c973056d6ba13ccbf537979c6cccf85358cb534621423c0f0e798751eff84fa2","target":"record","created_at":"2026-07-05T00:06:22Z","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":"3f8bd999e35a71133072d0d5f1f6f2285aaeb7e6522fac474641146b44b9f61a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-23T20:10:56Z","title_canon_sha256":"ae947d946cef197b2f76418660e2d4ba47f23b2c80befb186f28584af20f1da9"},"schema_version":"1.0","source":{"id":"1909.10599","kind":"arxiv","version":1}},"canonical_sha256":"635af4fcb4dc2db1078b64c4745dbd6ea168d5a26cbf23310c03278f66e3684b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"635af4fcb4dc2db1078b64c4745dbd6ea168d5a26cbf23310c03278f66e3684b","first_computed_at":"2026-07-05T00:06:22.218506Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:06:22.218506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2hHvDQXcKMJWnqjk8Zr9zaz2LMAyVWFZ0iMA42Q/8ullpaZ1smPqgO5M7WtiV1DFW9VrIkMdUexuIrAfBgCTBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:06:22.219037Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.10599","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c973056d6ba13ccbf537979c6cccf85358cb534621423c0f0e798751eff84fa2","sha256:5b3054f693b92300a93d5e3a9d08068e73eeeea656a732e26bf1dd574e2969d4"],"state_sha256":"4005601efd9ada2055f92be491aaea8d83e99ca7501ddc0781acb78e2232b023"}