{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:HFZLMG2Q4FZ6COEU7FURETRYMG","short_pith_number":"pith:HFZLMG2Q","canonical_record":{"source":{"id":"1506.05865","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-19T02:40:42Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"e101eee62de652f9195623c93f30d9774ef90d698650ef36c2ec076cf0875675","abstract_canon_sha256":"8fbd676b27629de3d74cf4d8e3331b4500bd98af445aa446648133f270215085"},"schema_version":"1.0"},"canonical_sha256":"3972b61b50e173e13894f969124e38618b7b2e9eac2e203ae14fa5bc7bc0b851","source":{"kind":"arxiv","id":"1506.05865","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.05865","created_at":"2026-05-18T01:20:19Z"},{"alias_kind":"arxiv_version","alias_value":"1506.05865v4","created_at":"2026-05-18T01:20:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.05865","created_at":"2026-05-18T01:20:19Z"},{"alias_kind":"pith_short_12","alias_value":"HFZLMG2Q4FZ6","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"HFZLMG2Q4FZ6COEU","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"HFZLMG2Q","created_at":"2026-05-18T12:29:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:HFZLMG2Q4FZ6COEU7FURETRYMG","target":"record","payload":{"canonical_record":{"source":{"id":"1506.05865","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-19T02:40:42Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"e101eee62de652f9195623c93f30d9774ef90d698650ef36c2ec076cf0875675","abstract_canon_sha256":"8fbd676b27629de3d74cf4d8e3331b4500bd98af445aa446648133f270215085"},"schema_version":"1.0"},"canonical_sha256":"3972b61b50e173e13894f969124e38618b7b2e9eac2e203ae14fa5bc7bc0b851","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:19.077652Z","signature_b64":"9SGKeh9z4In/Xe047UnGulC/IzfulO7M41GdKirjtPKKODvNFn/UAOedpwaLlzJqTOzf5kvPR7noRVDsVYONDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3972b61b50e173e13894f969124e38618b7b2e9eac2e203ae14fa5bc7bc0b851","last_reissued_at":"2026-05-18T01:20:19.077214Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:19.077214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.05865","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:20:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wmCODdewEmiz7Df8x8NhskysMxE/4UguhFOdzNVcbfGFETRvRhVloqGeLEEruO1LQNZmpPW+WzAhTHq+CtSJBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:18:26.218595Z"},"content_sha256":"362d2c610fa768eec88cbe7cd6bfca44093cf49abcd05c5101d17508fe2f3cf3","schema_version":"1.0","event_id":"sha256:362d2c610fa768eec88cbe7cd6bfca44093cf49abcd05c5101d17508fe2f3cf3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:HFZLMG2Q4FZ6COEU7FURETRYMG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LCSTS: A Large Scale Chinese Short Text Summarization Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Baotian Hu, Fangze Zhu, Qingcai Chen","submitted_at":"2015-06-19T02:40:42Z","abstract_excerpt":"Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public {http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.05865","kind":"arxiv","version":4},"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":"2026-05-18T01:20:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+/cw+6cBVZiQQ7TDVcVfANJIC7GaCmGjAFqm1/ULNWrLDC0P4Fmo9/4OdHxstXIC/KZdSZtF7yzfSxc5/zMzDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:18:26.219127Z"},"content_sha256":"95060308f06aebe4fa891544e28da0a3f7b9b078e1a95e946a09bae1820e4f54","schema_version":"1.0","event_id":"sha256:95060308f06aebe4fa891544e28da0a3f7b9b078e1a95e946a09bae1820e4f54"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HFZLMG2Q4FZ6COEU7FURETRYMG/bundle.json","state_url":"https://pith.science/pith/HFZLMG2Q4FZ6COEU7FURETRYMG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HFZLMG2Q4FZ6COEU7FURETRYMG/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":"2026-05-31T22:18:26Z","links":{"resolver":"https://pith.science/pith/HFZLMG2Q4FZ6COEU7FURETRYMG","bundle":"https://pith.science/pith/HFZLMG2Q4FZ6COEU7FURETRYMG/bundle.json","state":"https://pith.science/pith/HFZLMG2Q4FZ6COEU7FURETRYMG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HFZLMG2Q4FZ6COEU7FURETRYMG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:HFZLMG2Q4FZ6COEU7FURETRYMG","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":"8fbd676b27629de3d74cf4d8e3331b4500bd98af445aa446648133f270215085","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-19T02:40:42Z","title_canon_sha256":"e101eee62de652f9195623c93f30d9774ef90d698650ef36c2ec076cf0875675"},"schema_version":"1.0","source":{"id":"1506.05865","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.05865","created_at":"2026-05-18T01:20:19Z"},{"alias_kind":"arxiv_version","alias_value":"1506.05865v4","created_at":"2026-05-18T01:20:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.05865","created_at":"2026-05-18T01:20:19Z"},{"alias_kind":"pith_short_12","alias_value":"HFZLMG2Q4FZ6","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"HFZLMG2Q4FZ6COEU","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"HFZLMG2Q","created_at":"2026-05-18T12:29:25Z"}],"graph_snapshots":[{"event_id":"sha256:95060308f06aebe4fa891544e28da0a3f7b9b078e1a95e946a09bae1820e4f54","target":"graph","created_at":"2026-05-18T01:20:19Z","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":"Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public {http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually","authors_text":"Baotian Hu, Fangze Zhu, Qingcai Chen","cross_cats":["cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-19T02:40:42Z","title":"LCSTS: A Large Scale Chinese Short Text Summarization Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.05865","kind":"arxiv","version":4},"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":"sha256:362d2c610fa768eec88cbe7cd6bfca44093cf49abcd05c5101d17508fe2f3cf3","target":"record","created_at":"2026-05-18T01:20:19Z","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":"8fbd676b27629de3d74cf4d8e3331b4500bd98af445aa446648133f270215085","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-19T02:40:42Z","title_canon_sha256":"e101eee62de652f9195623c93f30d9774ef90d698650ef36c2ec076cf0875675"},"schema_version":"1.0","source":{"id":"1506.05865","kind":"arxiv","version":4}},"canonical_sha256":"3972b61b50e173e13894f969124e38618b7b2e9eac2e203ae14fa5bc7bc0b851","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3972b61b50e173e13894f969124e38618b7b2e9eac2e203ae14fa5bc7bc0b851","first_computed_at":"2026-05-18T01:20:19.077214Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:19.077214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9SGKeh9z4In/Xe047UnGulC/IzfulO7M41GdKirjtPKKODvNFn/UAOedpwaLlzJqTOzf5kvPR7noRVDsVYONDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:19.077652Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.05865","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:362d2c610fa768eec88cbe7cd6bfca44093cf49abcd05c5101d17508fe2f3cf3","sha256:95060308f06aebe4fa891544e28da0a3f7b9b078e1a95e946a09bae1820e4f54"],"state_sha256":"a3f7e5e909d0b687c1b44bb5c2a57c140e9a01161302190129361ce5edbe5c84"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OxqcERsncVg1lvDtolpyokrKrAZkiWCZyOTHPz143H6LN/xiM+x//IMWcbXfcdqDtoM068PW3xTEHuiTtcppBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T22:18:26.221925Z","bundle_sha256":"087889d63ed2f0c4bd552a90a51e762b2c5c3e9e6a6a0e3df6741b40611b8fa0"}}