{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:AMIQ2QLSMMSZQDTELSDFXUFGEN","short_pith_number":"pith:AMIQ2QLS","schema_version":"1.0","canonical_sha256":"03110d41726325980e645c865bd0a62353ad3a00435cdd7ac02168464c7da985","source":{"kind":"arxiv","id":"1708.02210","version":1},"attestation_state":"computed","paper":{"title":"Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowd-sourced Time-Sync Comments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Chaomei Chen, Qing Ping","submitted_at":"2017-08-07T17:21:20Z","abstract_excerpt":"With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1708.02210","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-07T17:21:20Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"90ca9d0daf47ee564341ea3837fc1c6fc5c02edffe1b6ee00ac0f3a4e44f86b1","abstract_canon_sha256":"581131e5671ccf06d48197f3e5db3c987666e145e93b6107aad1ee2fd1fa92f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:31.783305Z","signature_b64":"cpmXUQOm5MYzgqSKOGsol4bS91F8SmlwN6llIh/7wytQlyWhU8Az0aWa5MJU6zRqhre47ouy5rXrkI0IJstMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03110d41726325980e645c865bd0a62353ad3a00435cdd7ac02168464c7da985","last_reissued_at":"2026-05-18T00:38:31.782913Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:31.782913Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowd-sourced Time-Sync Comments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Chaomei Chen, Qing Ping","submitted_at":"2017-08-07T17:21:20Z","abstract_excerpt":"With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02210","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1708.02210","created_at":"2026-05-18T00:38:31.782968+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.02210v1","created_at":"2026-05-18T00:38:31.782968+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02210","created_at":"2026-05-18T00:38:31.782968+00:00"},{"alias_kind":"pith_short_12","alias_value":"AMIQ2QLSMMSZ","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"AMIQ2QLSMMSZQDTE","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"AMIQ2QLS","created_at":"2026-05-18T12:31:05.417338+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN","json":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN.json","graph_json":"https://pith.science/api/pith-number/AMIQ2QLSMMSZQDTELSDFXUFGEN/graph.json","events_json":"https://pith.science/api/pith-number/AMIQ2QLSMMSZQDTELSDFXUFGEN/events.json","paper":"https://pith.science/paper/AMIQ2QLS"},"agent_actions":{"view_html":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN","download_json":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN.json","view_paper":"https://pith.science/paper/AMIQ2QLS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.02210&json=true","fetch_graph":"https://pith.science/api/pith-number/AMIQ2QLSMMSZQDTELSDFXUFGEN/graph.json","fetch_events":"https://pith.science/api/pith-number/AMIQ2QLSMMSZQDTELSDFXUFGEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN/action/storage_attestation","attest_author":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN/action/author_attestation","sign_citation":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN/action/citation_signature","submit_replication":"https://pith.science/pith/AMIQ2QLSMMSZQDTELSDFXUFGEN/action/replication_record"}},"created_at":"2026-05-18T00:38:31.782968+00:00","updated_at":"2026-05-18T00:38:31.782968+00:00"}