{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:GBR7UXZ3VYLCVALUZCHYCLTXEQ","short_pith_number":"pith:GBR7UXZ3","canonical_record":{"source":{"id":"2010.02377","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T22:49:16Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"81b1587b02dea62eec8ed385e80c16e70c1d85c5985c835f7e6ac7938f3f9733","abstract_canon_sha256":"7eb0b2ec5019670b305a82b083ecba96dd2b2b7895b41750d7ac486a5b07e338"},"schema_version":"1.0"},"canonical_sha256":"3063fa5f3bae162a8174c88f812e772419cd1707b49948db71be6c0b36e02b16","source":{"kind":"arxiv","id":"2010.02377","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.02377","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"arxiv_version","alias_value":"2010.02377v1","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.02377","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"pith_short_12","alias_value":"GBR7UXZ3VYLC","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"pith_short_16","alias_value":"GBR7UXZ3VYLCVALU","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"pith_short_8","alias_value":"GBR7UXZ3","created_at":"2026-07-05T01:40:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:GBR7UXZ3VYLCVALUZCHYCLTXEQ","target":"record","payload":{"canonical_record":{"source":{"id":"2010.02377","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T22:49:16Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"81b1587b02dea62eec8ed385e80c16e70c1d85c5985c835f7e6ac7938f3f9733","abstract_canon_sha256":"7eb0b2ec5019670b305a82b083ecba96dd2b2b7895b41750d7ac486a5b07e338"},"schema_version":"1.0"},"canonical_sha256":"3063fa5f3bae162a8174c88f812e772419cd1707b49948db71be6c0b36e02b16","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:40:49.188566Z","signature_b64":"p2FDJWBxGIKcLWzWD+in7iXo43p4Xsai0/BJ2TVDpH6CTTc31ZgaMMMydD3fFj9L2sBGkPu6uJSjqqVKxwxEBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3063fa5f3bae162a8174c88f812e772419cd1707b49948db71be6c0b36e02b16","last_reissued_at":"2026-07-05T01:40:49.188208Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:40:49.188208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.02377","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":"2026-07-05T01:40:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VZBP9FTFdlDJ3uD0M5s/+UCVhZTqHcJEuGOf8EZ9Mp+MisVWFBxzP4P8WpHDZs0R5cfYVljQ9SS6vUsCsQ6rBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:35:20.974543Z"},"content_sha256":"be0e247c85d4719b1f224b1f9e98f9dbd689e73680f4f1450c505545c75ab015","schema_version":"1.0","event_id":"sha256:be0e247c85d4719b1f224b1f9e98f9dbd689e73680f4f1450c505545c75ab015"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:GBR7UXZ3VYLCVALUZCHYCLTXEQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Neural Topic Models using Knowledge Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Alexander Hoyle, Philip Resnik, Pranav Goel","submitted_at":"2020-10-05T22:49:16Z","abstract_excerpt":"Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we demonstrate using two models having disparate architectures, obtaining state-of-the-art topic coherence. We show that our adaptable framework not only improves performance in the aggregate over all estimated topics, as is commonly reported, but als"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.02377","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/2010.02377/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":"2026-07-05T01:40:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mZJanwXXpl0LmgRXMd9cdQD2Xa1B50784l26q33oZaUPQzWc2x08Yfy47wqE2ibOiG5foQ3rUwdo6odHEK8BCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:35:20.975064Z"},"content_sha256":"b6170d070a54e954a91a6d90ef6734604342bf8b02c9e6a1a03124eb8b07bace","schema_version":"1.0","event_id":"sha256:b6170d070a54e954a91a6d90ef6734604342bf8b02c9e6a1a03124eb8b07bace"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GBR7UXZ3VYLCVALUZCHYCLTXEQ/bundle.json","state_url":"https://pith.science/pith/GBR7UXZ3VYLCVALUZCHYCLTXEQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GBR7UXZ3VYLCVALUZCHYCLTXEQ/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":"2026-08-04T21:35:20Z","links":{"resolver":"https://pith.science/pith/GBR7UXZ3VYLCVALUZCHYCLTXEQ","bundle":"https://pith.science/pith/GBR7UXZ3VYLCVALUZCHYCLTXEQ/bundle.json","state":"https://pith.science/pith/GBR7UXZ3VYLCVALUZCHYCLTXEQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GBR7UXZ3VYLCVALUZCHYCLTXEQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:GBR7UXZ3VYLCVALUZCHYCLTXEQ","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":"7eb0b2ec5019670b305a82b083ecba96dd2b2b7895b41750d7ac486a5b07e338","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T22:49:16Z","title_canon_sha256":"81b1587b02dea62eec8ed385e80c16e70c1d85c5985c835f7e6ac7938f3f9733"},"schema_version":"1.0","source":{"id":"2010.02377","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.02377","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"arxiv_version","alias_value":"2010.02377v1","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.02377","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"pith_short_12","alias_value":"GBR7UXZ3VYLC","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"pith_short_16","alias_value":"GBR7UXZ3VYLCVALU","created_at":"2026-07-05T01:40:49Z"},{"alias_kind":"pith_short_8","alias_value":"GBR7UXZ3","created_at":"2026-07-05T01:40:49Z"}],"graph_snapshots":[{"event_id":"sha256:b6170d070a54e954a91a6d90ef6734604342bf8b02c9e6a1a03124eb8b07bace","target":"graph","created_at":"2026-07-05T01:40:49Z","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/2010.02377/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we demonstrate using two models having disparate architectures, obtaining state-of-the-art topic coherence. We show that our adaptable framework not only improves performance in the aggregate over all estimated topics, as is commonly reported, but als","authors_text":"Alexander Hoyle, Philip Resnik, Pranav Goel","cross_cats":["cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T22:49:16Z","title":"Improving Neural Topic Models using Knowledge Distillation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.02377","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:be0e247c85d4719b1f224b1f9e98f9dbd689e73680f4f1450c505545c75ab015","target":"record","created_at":"2026-07-05T01:40:49Z","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":"7eb0b2ec5019670b305a82b083ecba96dd2b2b7895b41750d7ac486a5b07e338","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T22:49:16Z","title_canon_sha256":"81b1587b02dea62eec8ed385e80c16e70c1d85c5985c835f7e6ac7938f3f9733"},"schema_version":"1.0","source":{"id":"2010.02377","kind":"arxiv","version":1}},"canonical_sha256":"3063fa5f3bae162a8174c88f812e772419cd1707b49948db71be6c0b36e02b16","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3063fa5f3bae162a8174c88f812e772419cd1707b49948db71be6c0b36e02b16","first_computed_at":"2026-07-05T01:40:49.188208Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:40:49.188208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p2FDJWBxGIKcLWzWD+in7iXo43p4Xsai0/BJ2TVDpH6CTTc31ZgaMMMydD3fFj9L2sBGkPu6uJSjqqVKxwxEBw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:40:49.188566Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.02377","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:be0e247c85d4719b1f224b1f9e98f9dbd689e73680f4f1450c505545c75ab015","sha256:b6170d070a54e954a91a6d90ef6734604342bf8b02c9e6a1a03124eb8b07bace"],"state_sha256":"e1f725f09365a3739d9e2abe22d1bb5724345ac6a67732341a0dbbc6508020b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IjD4nn5L+xAtNjDSCJXA18qft9BQb4dB77iz+UwTJIylso5/nAN+/BrKMOukbeiurwl3S4AOzTcEzs0qKxaPCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T21:35:20.979275Z","bundle_sha256":"15dc891b1f1f97a9771effdd36f76be52da650c57ce98956c717ac78832be771"}}