{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:HCYDYMSEMHTGBPXUFUBKXEOBM5","short_pith_number":"pith:HCYDYMSE","canonical_record":{"source":{"id":"2205.11973","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2022-05-24T11:15:35Z","cross_cats_sorted":[],"title_canon_sha256":"11c8abb29012ece69a82e4e05bd6aaf21bf61f9c6e456fac516b0dc789cd73b3","abstract_canon_sha256":"c980999daa3a7e4810c33c06990c80e17f1dff7a03c398cc56ef6cda8a674e00"},"schema_version":"1.0"},"canonical_sha256":"38b03c324461e660bef42d02ab91c16745be47ed0481fb4d576c47e67e15dbb1","source":{"kind":"arxiv","id":"2205.11973","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.11973","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"arxiv_version","alias_value":"2205.11973v1","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.11973","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"pith_short_12","alias_value":"HCYDYMSEMHTG","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"pith_short_16","alias_value":"HCYDYMSEMHTGBPXU","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"pith_short_8","alias_value":"HCYDYMSE","created_at":"2026-07-05T04:26:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:HCYDYMSEMHTGBPXUFUBKXEOBM5","target":"record","payload":{"canonical_record":{"source":{"id":"2205.11973","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2022-05-24T11:15:35Z","cross_cats_sorted":[],"title_canon_sha256":"11c8abb29012ece69a82e4e05bd6aaf21bf61f9c6e456fac516b0dc789cd73b3","abstract_canon_sha256":"c980999daa3a7e4810c33c06990c80e17f1dff7a03c398cc56ef6cda8a674e00"},"schema_version":"1.0"},"canonical_sha256":"38b03c324461e660bef42d02ab91c16745be47ed0481fb4d576c47e67e15dbb1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:26:10.161735Z","signature_b64":"hPuu3XM91VInHIQS9CLiWhWDyG4mS5Q80uGTKTWhsnOvzy0vJ+giKkqrEBaxD7d1KAb9nqCTPQL/7J8zzmzhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38b03c324461e660bef42d02ab91c16745be47ed0481fb4d576c47e67e15dbb1","last_reissued_at":"2026-07-05T04:26:10.161341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:26:10.161341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.11973","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":"2026-07-05T04:26:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SvTbyiS9G0Ha/ee1V2Q9bhXffKz9gwz4mTxi5llnG776cqTrEXbJf4wRI1tcNM0ag3+GA965Qk2EjogmrQOEBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T19:35:06.303936Z"},"content_sha256":"425d0821f57f6ade25e34ecad1430b0b72272373964d5ee8b01dca63acff1fb8","schema_version":"1.0","event_id":"sha256:425d0821f57f6ade25e34ecad1430b0b72272373964d5ee8b01dca63acff1fb8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:HCYDYMSEMHTGBPXUFUBKXEOBM5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Huiling Song, Jucheng Yang, Peng Huo, Tao Xu, Tingting Zhao, Yarui Chen, Yuan Wang","submitted_at":"2022-05-24T11:15:35Z","abstract_excerpt":"Extreme multi-label text classification (XMTC) refers to the problem of tagging a given text with the most relevant subset of labels from a large label set. A majority of labels only have a few training instances due to large label dimensionality in XMTC. To solve this data sparsity issue, most existing XMTC methods take advantage of fixed label clusters obtained in early stage to balance performance on tail labels and head labels. However, such label clusters provide static and coarse-grained semantic scope for every text, which ignores distinct characteristics of different texts and has diff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.11973","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/2205.11973/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":"2026-07-05T04:26:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q65GZ4Ii0I8JryghXw8N7kRKFOOK2RbviOZoBIyixqIBcpQKQeU/VXCkP7TUpNb8w23JvdRZN2SoX48tQFkSAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T19:35:06.304515Z"},"content_sha256":"bc24fb4b0f6e4ed297283a796bb4db87dff292198c219c2e68c838b17fb963df","schema_version":"1.0","event_id":"sha256:bc24fb4b0f6e4ed297283a796bb4db87dff292198c219c2e68c838b17fb963df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HCYDYMSEMHTGBPXUFUBKXEOBM5/bundle.json","state_url":"https://pith.science/pith/HCYDYMSEMHTGBPXUFUBKXEOBM5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HCYDYMSEMHTGBPXUFUBKXEOBM5/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":"2026-08-10T19:35:06Z","links":{"resolver":"https://pith.science/pith/HCYDYMSEMHTGBPXUFUBKXEOBM5","bundle":"https://pith.science/pith/HCYDYMSEMHTGBPXUFUBKXEOBM5/bundle.json","state":"https://pith.science/pith/HCYDYMSEMHTGBPXUFUBKXEOBM5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HCYDYMSEMHTGBPXUFUBKXEOBM5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HCYDYMSEMHTGBPXUFUBKXEOBM5","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":"c980999daa3a7e4810c33c06990c80e17f1dff7a03c398cc56ef6cda8a674e00","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2022-05-24T11:15:35Z","title_canon_sha256":"11c8abb29012ece69a82e4e05bd6aaf21bf61f9c6e456fac516b0dc789cd73b3"},"schema_version":"1.0","source":{"id":"2205.11973","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.11973","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"arxiv_version","alias_value":"2205.11973v1","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.11973","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"pith_short_12","alias_value":"HCYDYMSEMHTG","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"pith_short_16","alias_value":"HCYDYMSEMHTGBPXU","created_at":"2026-07-05T04:26:10Z"},{"alias_kind":"pith_short_8","alias_value":"HCYDYMSE","created_at":"2026-07-05T04:26:10Z"}],"graph_snapshots":[{"event_id":"sha256:bc24fb4b0f6e4ed297283a796bb4db87dff292198c219c2e68c838b17fb963df","target":"graph","created_at":"2026-07-05T04:26:10Z","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/2205.11973/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Extreme multi-label text classification (XMTC) refers to the problem of tagging a given text with the most relevant subset of labels from a large label set. A majority of labels only have a few training instances due to large label dimensionality in XMTC. To solve this data sparsity issue, most existing XMTC methods take advantage of fixed label clusters obtained in early stage to balance performance on tail labels and head labels. However, such label clusters provide static and coarse-grained semantic scope for every text, which ignores distinct characteristics of different texts and has diff","authors_text":"Huiling Song, Jucheng Yang, Peng Huo, Tao Xu, Tingting Zhao, Yarui Chen, Yuan Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2022-05-24T11:15:35Z","title":"Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.11973","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":"sha256:425d0821f57f6ade25e34ecad1430b0b72272373964d5ee8b01dca63acff1fb8","target":"record","created_at":"2026-07-05T04:26:10Z","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":"c980999daa3a7e4810c33c06990c80e17f1dff7a03c398cc56ef6cda8a674e00","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2022-05-24T11:15:35Z","title_canon_sha256":"11c8abb29012ece69a82e4e05bd6aaf21bf61f9c6e456fac516b0dc789cd73b3"},"schema_version":"1.0","source":{"id":"2205.11973","kind":"arxiv","version":1}},"canonical_sha256":"38b03c324461e660bef42d02ab91c16745be47ed0481fb4d576c47e67e15dbb1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38b03c324461e660bef42d02ab91c16745be47ed0481fb4d576c47e67e15dbb1","first_computed_at":"2026-07-05T04:26:10.161341Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:26:10.161341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hPuu3XM91VInHIQS9CLiWhWDyG4mS5Q80uGTKTWhsnOvzy0vJ+giKkqrEBaxD7d1KAb9nqCTPQL/7J8zzmzhDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:26:10.161735Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.11973","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:425d0821f57f6ade25e34ecad1430b0b72272373964d5ee8b01dca63acff1fb8","sha256:bc24fb4b0f6e4ed297283a796bb4db87dff292198c219c2e68c838b17fb963df"],"state_sha256":"714088aafa943959665d47d11628e3c2a35aefcaee7ed034ba1985116b0a7c96"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LATI1jCX+T+NnjS4ni655H0kS3arRczTPP7mkzlAEil2S7BLzgt+njxoxTuEDHciELZLI/FlTJpMDTBMrMlYAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T19:35:06.308230Z","bundle_sha256":"02474727d366bc4ea001a85f396539cb0a1f855dcc69f7b3d543e73f1f6d7a3a"}}