{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NK25BSIJUS5I24GDVKBTBU4Q2J","short_pith_number":"pith:NK25BSIJ","canonical_record":{"source":{"id":"1903.09424","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-03-22T09:54:36Z","cross_cats_sorted":[],"title_canon_sha256":"6f10c7c65ef64d0c652030183e18684557c517928a960ffa3cd99849eab3499a","abstract_canon_sha256":"0e72a76f352bdb705c63d896d03ac47b5114509d88f315a8521ffec019b0f371"},"schema_version":"1.0"},"canonical_sha256":"6ab5d0c909a4ba8d70c3aa8330d390d267160777b7039166a32d78924cf7afc8","source":{"kind":"arxiv","id":"1903.09424","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.09424","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"arxiv_version","alias_value":"1903.09424v1","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09424","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"pith_short_12","alias_value":"NK25BSIJUS5I","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NK25BSIJUS5I24GD","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NK25BSIJ","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NK25BSIJUS5I24GDVKBTBU4Q2J","target":"record","payload":{"canonical_record":{"source":{"id":"1903.09424","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-03-22T09:54:36Z","cross_cats_sorted":[],"title_canon_sha256":"6f10c7c65ef64d0c652030183e18684557c517928a960ffa3cd99849eab3499a","abstract_canon_sha256":"0e72a76f352bdb705c63d896d03ac47b5114509d88f315a8521ffec019b0f371"},"schema_version":"1.0"},"canonical_sha256":"6ab5d0c909a4ba8d70c3aa8330d390d267160777b7039166a32d78924cf7afc8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:39.671255Z","signature_b64":"v540mfnlm7r/zKA3ETTvtvW9DOPF7ZSiJXfdEMRAXVtckHpGJ4r0WKpsxmGskqFAKsurBHTEkXOy+O5OtSaUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ab5d0c909a4ba8d70c3aa8330d390d267160777b7039166a32d78924cf7afc8","last_reissued_at":"2026-05-17T23:50:39.670774Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:39.670774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.09424","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":"2026-05-17T23:50:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R3Vq9Rs80q2izVEXBCoiK6WzRb0AVlyjRXjika0n9507AYaCgYoQ0YI44L+emMgcqi784z9NdGLEffGD+kKmAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:03:13.154645Z"},"content_sha256":"5fa0a06db1284203c1e326dbc84462e0cd87541b68b62de3a08f069c18a7d489","schema_version":"1.0","event_id":"sha256:5fa0a06db1284203c1e326dbc84462e0cd87541b68b62de3a08f069c18a7d489"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NK25BSIJUS5I24GDVKBTBU4Q2J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An end-to-end Neural Network Framework for Text Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jie Zhou, Jinchao Zhang, Xingyi Cheng","submitted_at":"2019-03-22T09:54:36Z","abstract_excerpt":"The unsupervised text clustering is one of the major tasks in natural language processing (NLP) and remains a difficult and complex problem. Conventional \\mbox{methods} generally treat this task using separated steps, including text representation learning and clustering the representations. As an improvement, neural methods have also been introduced for continuous representation learning to address the sparsity problem. However, the multi-step process still deviates from the unified optimization target. Especially the second step of cluster is generally performed with conventional methods suc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09424","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":""},"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":"2026-05-17T23:50:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t0qPOUqCGPgeAFZ8D0TnDoygZChoH/w+eJ82c4RFZ7jJmFiM/q+Gwmnm1sBkzdxly36s04g3lofxBvCa3fVPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:03:13.155003Z"},"content_sha256":"b514777782ea4d7141dca5337edc224f3439bc5f12d51986b129f75b64fe325b","schema_version":"1.0","event_id":"sha256:b514777782ea4d7141dca5337edc224f3439bc5f12d51986b129f75b64fe325b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NK25BSIJUS5I24GDVKBTBU4Q2J/bundle.json","state_url":"https://pith.science/pith/NK25BSIJUS5I24GDVKBTBU4Q2J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NK25BSIJUS5I24GDVKBTBU4Q2J/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":"2026-06-02T22:03:13Z","links":{"resolver":"https://pith.science/pith/NK25BSIJUS5I24GDVKBTBU4Q2J","bundle":"https://pith.science/pith/NK25BSIJUS5I24GDVKBTBU4Q2J/bundle.json","state":"https://pith.science/pith/NK25BSIJUS5I24GDVKBTBU4Q2J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NK25BSIJUS5I24GDVKBTBU4Q2J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NK25BSIJUS5I24GDVKBTBU4Q2J","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":"0e72a76f352bdb705c63d896d03ac47b5114509d88f315a8521ffec019b0f371","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-03-22T09:54:36Z","title_canon_sha256":"6f10c7c65ef64d0c652030183e18684557c517928a960ffa3cd99849eab3499a"},"schema_version":"1.0","source":{"id":"1903.09424","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.09424","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"arxiv_version","alias_value":"1903.09424v1","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09424","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"pith_short_12","alias_value":"NK25BSIJUS5I","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NK25BSIJUS5I24GD","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NK25BSIJ","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:b514777782ea4d7141dca5337edc224f3439bc5f12d51986b129f75b64fe325b","target":"graph","created_at":"2026-05-17T23:50:39Z","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"},"paper":{"abstract_excerpt":"The unsupervised text clustering is one of the major tasks in natural language processing (NLP) and remains a difficult and complex problem. Conventional \\mbox{methods} generally treat this task using separated steps, including text representation learning and clustering the representations. As an improvement, neural methods have also been introduced for continuous representation learning to address the sparsity problem. However, the multi-step process still deviates from the unified optimization target. Especially the second step of cluster is generally performed with conventional methods suc","authors_text":"Jie Zhou, Jinchao Zhang, Xingyi Cheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-03-22T09:54:36Z","title":"An end-to-end Neural Network Framework for Text Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09424","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":"sha256:5fa0a06db1284203c1e326dbc84462e0cd87541b68b62de3a08f069c18a7d489","target":"record","created_at":"2026-05-17T23:50:39Z","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":"0e72a76f352bdb705c63d896d03ac47b5114509d88f315a8521ffec019b0f371","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-03-22T09:54:36Z","title_canon_sha256":"6f10c7c65ef64d0c652030183e18684557c517928a960ffa3cd99849eab3499a"},"schema_version":"1.0","source":{"id":"1903.09424","kind":"arxiv","version":1}},"canonical_sha256":"6ab5d0c909a4ba8d70c3aa8330d390d267160777b7039166a32d78924cf7afc8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6ab5d0c909a4ba8d70c3aa8330d390d267160777b7039166a32d78924cf7afc8","first_computed_at":"2026-05-17T23:50:39.670774Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:50:39.670774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v540mfnlm7r/zKA3ETTvtvW9DOPF7ZSiJXfdEMRAXVtckHpGJ4r0WKpsxmGskqFAKsurBHTEkXOy+O5OtSaUDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:50:39.671255Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.09424","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5fa0a06db1284203c1e326dbc84462e0cd87541b68b62de3a08f069c18a7d489","sha256:b514777782ea4d7141dca5337edc224f3439bc5f12d51986b129f75b64fe325b"],"state_sha256":"77c811fbfa292f00709f9320806198cdffd321a25df773c4551240f72c514979"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"icVdbgnMXzJRQawumnhV0MOs2OQKv4XCz1gKvjNDMWEJnEFgMlSoce+iZrLrQ3SYDz2H0+lggw6APbCANhYZBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T22:03:13.157410Z","bundle_sha256":"691f56c3c51ae8d8a830bd4c6809bcaa7c43fc32f229bbebe44bb8bd7273adb0"}}