{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:5UXLYTYAK4PIXICMBUMHV2NXXD","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":"bf63f764ec4f2e1cddec6e070b7def6dfff7e8ddb025503d288279a94765d320","cross_cats_sorted":["cs.AI","cs.CE","cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-02T03:40:17Z","title_canon_sha256":"ef9072993bcd3239f23b07a9f24b8a8322a4cb973d5c2c5b07f40fd259aae58b"},"schema_version":"1.0","source":{"id":"2112.00963","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.00963","created_at":"2026-07-05T03:37:30Z"},{"alias_kind":"arxiv_version","alias_value":"2112.00963v2","created_at":"2026-07-05T03:37:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.00963","created_at":"2026-07-05T03:37:30Z"},{"alias_kind":"pith_short_12","alias_value":"5UXLYTYAK4PI","created_at":"2026-07-05T03:37:30Z"},{"alias_kind":"pith_short_16","alias_value":"5UXLYTYAK4PIXICM","created_at":"2026-07-05T03:37:30Z"},{"alias_kind":"pith_short_8","alias_value":"5UXLYTYA","created_at":"2026-07-05T03:37:30Z"}],"graph_snapshots":[{"event_id":"sha256:179d64d76cfa678ce31ec0cdd9718926ea28794295ab0605605c7ce360d14be1","target":"graph","created_at":"2026-07-05T03:37:30Z","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/2112.00963/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals. The recent emergence of deep learning techniques has shown great promise in creating automated pipelines to benefit the EC-supported financial applications. However, these methods presume all included contents to be informative without refining valuable semantics from long-text transcript and suffer from EC scarcity issue. Meanwhile, these black-box methods possess inherent difficulties in pr","authors_text":"Guangnan Ye, Hui Xiong, Wei Zhang, Yada Zhu, Ziming Huang, Zixuan Yuan","cross_cats":["cs.AI","cs.CE","cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-02T03:40:17Z","title":"Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.00963","kind":"arxiv","version":2},"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:d240ad0934da5e8cd1ad2d22393ca5a683feb6b85fe22f3db462f0446a60777e","target":"record","created_at":"2026-07-05T03:37:30Z","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":"bf63f764ec4f2e1cddec6e070b7def6dfff7e8ddb025503d288279a94765d320","cross_cats_sorted":["cs.AI","cs.CE","cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-02T03:40:17Z","title_canon_sha256":"ef9072993bcd3239f23b07a9f24b8a8322a4cb973d5c2c5b07f40fd259aae58b"},"schema_version":"1.0","source":{"id":"2112.00963","kind":"arxiv","version":2}},"canonical_sha256":"ed2ebc4f00571e8ba04c0d187ae9b7b8f654a35ea864ed14bd6e47c8f4b80c92","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ed2ebc4f00571e8ba04c0d187ae9b7b8f654a35ea864ed14bd6e47c8f4b80c92","first_computed_at":"2026-07-05T03:37:30.384044Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:37:30.384044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kjTGw/e+7wg9k+hf+UCHuFI7zMD8Njb+reSIN2jmbqMbqGhbpiHECVAvrD9bwYd6kRMaZdspn9+PMwVGS8LpCA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:37:30.384585Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.00963","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d240ad0934da5e8cd1ad2d22393ca5a683feb6b85fe22f3db462f0446a60777e","sha256:179d64d76cfa678ce31ec0cdd9718926ea28794295ab0605605c7ce360d14be1"],"state_sha256":"de351e321305cc5f0d496b1fb39d26d163ebbce9479e2caa59033854d9d07704"}