{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:SJRKOIWQ5JFURUC4GJNO67CXFA","short_pith_number":"pith:SJRKOIWQ","canonical_record":{"source":{"id":"1807.03089","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-09T13:05:36Z","cross_cats_sorted":[],"title_canon_sha256":"5b3e8fadd4b4405b98147f81a158044dce361cacc82e8d35fb85d42da58d6713","abstract_canon_sha256":"8a36de5bc6a1af7293591628e5fc6abb0b4bbeb9a963bc010fc0d8cf49eedda9"},"schema_version":"1.0"},"canonical_sha256":"9262a722d0ea4b48d05c325aef7c57281adbdb5ba27f3d5e46462ec9b5cce8c9","source":{"kind":"arxiv","id":"1807.03089","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03089","created_at":"2026-05-18T00:06:37Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03089v3","created_at":"2026-05-18T00:06:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03089","created_at":"2026-05-18T00:06:37Z"},{"alias_kind":"pith_short_12","alias_value":"SJRKOIWQ5JFU","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SJRKOIWQ5JFURUC4","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SJRKOIWQ","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:SJRKOIWQ5JFURUC4GJNO67CXFA","target":"record","payload":{"canonical_record":{"source":{"id":"1807.03089","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-09T13:05:36Z","cross_cats_sorted":[],"title_canon_sha256":"5b3e8fadd4b4405b98147f81a158044dce361cacc82e8d35fb85d42da58d6713","abstract_canon_sha256":"8a36de5bc6a1af7293591628e5fc6abb0b4bbeb9a963bc010fc0d8cf49eedda9"},"schema_version":"1.0"},"canonical_sha256":"9262a722d0ea4b48d05c325aef7c57281adbdb5ba27f3d5e46462ec9b5cce8c9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:37.672751Z","signature_b64":"8egOie9cKXHWENRUdZgebMQL/J52w4m7jN/04O7XHrxwGkOsRHJwfHNAN7JY0koC1FwRGrJFTAdSBf+bAo7HAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9262a722d0ea4b48d05c325aef7c57281adbdb5ba27f3d5e46462ec9b5cce8c9","last_reissued_at":"2026-05-18T00:06:37.672203Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:37.672203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.03089","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l6t7KAelcf1P7sD4CtPqIFhkicKj0zjFhSsuMGREo+j4D9eNO0BpDZ55NFfqo2v4FFnaF0m8n6ypMRxc9BFmDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T11:37:23.922503Z"},"content_sha256":"4c82f315fb0fe5a6bc496e468c9aa45f4e17be15c74afa17b1a78303f2c2dbbd","schema_version":"1.0","event_id":"sha256:4c82f315fb0fe5a6bc496e468c9aa45f4e17be15c74afa17b1a78303f2c2dbbd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:SJRKOIWQ5JFURUC4GJNO67CXFA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Video Summarisation by Classification with Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea Cavallaro, Kaiyang Zhou, Tao Xiang","submitted_at":"2018-07-09T13:05:36Z","abstract_excerpt":"Most existing video summarisation methods are based on either supervised or unsupervised learning. In this paper, we propose a reinforcement learning-based weakly supervised method that exploits easy-to-obtain, video-level category labels and encourages summaries to contain category-related information and maintain category recognisability. Specifically, We formulate video summarisation as a sequential decision-making process and train a summarisation network with deep Q-learning (DQSN). A companion classification network is also trained to provide rewards for training the DQSN. With the class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03089","kind":"arxiv","version":3},"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":"2026-05-18T00:06:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OCsVR/v5aRbnSUhekBk4tLZ1Ho6Q4I3LZO8LuVrd+GR8a4nYE3BTyFtwoJn9O6pAd7e3H8TITrNDj1EJXJazCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T11:37:23.923124Z"},"content_sha256":"4e0993d8929ae0efa80c35253b218640ce9c5e0c0a310ca2ae6be5b69357241e","schema_version":"1.0","event_id":"sha256:4e0993d8929ae0efa80c35253b218640ce9c5e0c0a310ca2ae6be5b69357241e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SJRKOIWQ5JFURUC4GJNO67CXFA/bundle.json","state_url":"https://pith.science/pith/SJRKOIWQ5JFURUC4GJNO67CXFA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SJRKOIWQ5JFURUC4GJNO67CXFA/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":"2026-06-07T11:37:23Z","links":{"resolver":"https://pith.science/pith/SJRKOIWQ5JFURUC4GJNO67CXFA","bundle":"https://pith.science/pith/SJRKOIWQ5JFURUC4GJNO67CXFA/bundle.json","state":"https://pith.science/pith/SJRKOIWQ5JFURUC4GJNO67CXFA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SJRKOIWQ5JFURUC4GJNO67CXFA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SJRKOIWQ5JFURUC4GJNO67CXFA","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":"8a36de5bc6a1af7293591628e5fc6abb0b4bbeb9a963bc010fc0d8cf49eedda9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-09T13:05:36Z","title_canon_sha256":"5b3e8fadd4b4405b98147f81a158044dce361cacc82e8d35fb85d42da58d6713"},"schema_version":"1.0","source":{"id":"1807.03089","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.03089","created_at":"2026-05-18T00:06:37Z"},{"alias_kind":"arxiv_version","alias_value":"1807.03089v3","created_at":"2026-05-18T00:06:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03089","created_at":"2026-05-18T00:06:37Z"},{"alias_kind":"pith_short_12","alias_value":"SJRKOIWQ5JFU","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SJRKOIWQ5JFURUC4","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SJRKOIWQ","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:4e0993d8929ae0efa80c35253b218640ce9c5e0c0a310ca2ae6be5b69357241e","target":"graph","created_at":"2026-05-18T00:06:37Z","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":"Most existing video summarisation methods are based on either supervised or unsupervised learning. In this paper, we propose a reinforcement learning-based weakly supervised method that exploits easy-to-obtain, video-level category labels and encourages summaries to contain category-related information and maintain category recognisability. Specifically, We formulate video summarisation as a sequential decision-making process and train a summarisation network with deep Q-learning (DQSN). A companion classification network is also trained to provide rewards for training the DQSN. With the class","authors_text":"Andrea Cavallaro, Kaiyang Zhou, Tao Xiang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-09T13:05:36Z","title":"Video Summarisation by Classification with Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03089","kind":"arxiv","version":3},"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":"sha256:4c82f315fb0fe5a6bc496e468c9aa45f4e17be15c74afa17b1a78303f2c2dbbd","target":"record","created_at":"2026-05-18T00:06:37Z","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":"8a36de5bc6a1af7293591628e5fc6abb0b4bbeb9a963bc010fc0d8cf49eedda9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-09T13:05:36Z","title_canon_sha256":"5b3e8fadd4b4405b98147f81a158044dce361cacc82e8d35fb85d42da58d6713"},"schema_version":"1.0","source":{"id":"1807.03089","kind":"arxiv","version":3}},"canonical_sha256":"9262a722d0ea4b48d05c325aef7c57281adbdb5ba27f3d5e46462ec9b5cce8c9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9262a722d0ea4b48d05c325aef7c57281adbdb5ba27f3d5e46462ec9b5cce8c9","first_computed_at":"2026-05-18T00:06:37.672203Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:37.672203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8egOie9cKXHWENRUdZgebMQL/J52w4m7jN/04O7XHrxwGkOsRHJwfHNAN7JY0koC1FwRGrJFTAdSBf+bAo7HAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:37.672751Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.03089","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c82f315fb0fe5a6bc496e468c9aa45f4e17be15c74afa17b1a78303f2c2dbbd","sha256:4e0993d8929ae0efa80c35253b218640ce9c5e0c0a310ca2ae6be5b69357241e"],"state_sha256":"7a42c8e3941eab4fe655e7447d8dfb351fc5ad7d23947c7c9697c5189d2b0036"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0fbZ3ZkzndGSODTDjQO9cwSZdvs8cczcxYXjubxG/ayTqq5oejPIHlfx5giCpl+dkqTzULFvLKmyBOOFZBP9Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T11:37:23.926965Z","bundle_sha256":"2fdee1fd25c15ab2aa2c82f04161ec7ceacf28a4bb9c5f0feea2365522711d3f"}}