{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:LBYX3BZW3Y2DJI4U5PXEBJD3FA","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":"4cb7812e00ccb8d8607e23f9c48977438d093f895082e18d597ff66fadc3f58d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-16T11:16:07Z","title_canon_sha256":"ae49381358d1dfcd9942031295b3959b52f14f415f2ab5b46bed9d6659560358"},"schema_version":"1.0","source":{"id":"2310.10285","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10285","created_at":"2026-07-05T07:01:18Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10285v1","created_at":"2026-07-05T07:01:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10285","created_at":"2026-07-05T07:01:18Z"},{"alias_kind":"pith_short_12","alias_value":"LBYX3BZW3Y2D","created_at":"2026-07-05T07:01:18Z"},{"alias_kind":"pith_short_16","alias_value":"LBYX3BZW3Y2DJI4U","created_at":"2026-07-05T07:01:18Z"},{"alias_kind":"pith_short_8","alias_value":"LBYX3BZW","created_at":"2026-07-05T07:01:18Z"}],"graph_snapshots":[{"event_id":"sha256:8f1233b0a0c4be2ac4f53774aa8c31cbbf5fce8fb1d6355340ea0f6759d4b2bb","target":"graph","created_at":"2026-07-05T07:01:18Z","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/2310.10285/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dialogue summarization involves a wide range of scenarios and domains. However, existing methods generally only apply to specific scenarios or domains. In this study, we propose a new pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization. It adopts a multi-stage pre-training strategy to reduce the gap between the pre-training objective and fine-tuning objective. Specifically, we first conduct domain-aware pre-training using large-scale multi-scenario multi-domain dialogue data to enhance the adaptability of our pre-trained model. Then, we conduct task-o","authors_text":"Feifei Zhai, Gengyao Li, Junnan Zhu, Weixiao Zhou, Xianfu Cheng, Xinnian Liang, Zhoujun Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-16T11:16:07Z","title":"Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10285","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":"sha256:4c3d9845432763893441c1e819ff295cfe8daeef5d0226b3d9d93567d5473016","target":"record","created_at":"2026-07-05T07:01:18Z","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":"4cb7812e00ccb8d8607e23f9c48977438d093f895082e18d597ff66fadc3f58d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-16T11:16:07Z","title_canon_sha256":"ae49381358d1dfcd9942031295b3959b52f14f415f2ab5b46bed9d6659560358"},"schema_version":"1.0","source":{"id":"2310.10285","kind":"arxiv","version":1}},"canonical_sha256":"58717d8736de3434a394ebee40a47b281383a79fe95d70e135ed1e29d99ec1f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"58717d8736de3434a394ebee40a47b281383a79fe95d70e135ed1e29d99ec1f7","first_computed_at":"2026-07-05T07:01:18.072281Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:01:18.072281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wXTL2MK3euN6VZYcd8JUThBWWqCRv+wrAwgurjc+zrR4hSRovmccbqPuJFmMiOzrW5weea7z/6hqVyf3aOZABQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:01:18.072675Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.10285","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c3d9845432763893441c1e819ff295cfe8daeef5d0226b3d9d93567d5473016","sha256:8f1233b0a0c4be2ac4f53774aa8c31cbbf5fce8fb1d6355340ea0f6759d4b2bb"],"state_sha256":"17dedacda4db8573e10b5ec5a934684b68de3c809a26ea3138c3bfce11e56fcf"}