{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SARUDGRFOJGVYISA3VEH5HKYX7","short_pith_number":"pith:SARUDGRF","schema_version":"1.0","canonical_sha256":"9023419a25724d5c2240dd487e9d58bfdd75e49f378a74f5160ef09e1b26681a","source":{"kind":"arxiv","id":"2505.23908","version":2},"attestation_state":"computed","paper":{"title":"Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Ann Clifton, Azin Ghazimatin, Edgar Tanaka, Edward Ronan, Winstead Zhu","submitted_at":"2025-05-29T18:02:16Z","abstract_excerpt":"Discovering and evaluating long-form talk content such as videos and podcasts poses a significant challenge for users, as it requires a considerable time investment. Previews offer a practical solution by providing concise snippets that showcase key moments of the content, enabling users to make more informed and confident choices. We propose an LLM-based approach for generating podcast episode previews and deploy the solution at scale, serving hundreds of thousands of podcast previews in a real-world application. Comprehensive offline evaluations and online A/B testing demonstrate that LLM-ge"},"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":"2505.23908","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2025-05-29T18:02:16Z","cross_cats_sorted":[],"title_canon_sha256":"a10af35ad023bc919771c50ff6f95add3959c6e667dd75b323645c3692205758","abstract_canon_sha256":"2c5678a22cfd5026740f24062b021a1cf49b9b4ff15e986acee6aad577c26b17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:00.987097Z","signature_b64":"3xhY1AmFu7iVSwZeWL6oQ3fHEXJazmd4oaF5yYPvnw/9K8o9G0+dtBRFYuSrycINQUZIGfCPJz0okEgKOixhAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9023419a25724d5c2240dd487e9d58bfdd75e49f378a74f5160ef09e1b26681a","last_reissued_at":"2026-07-05T11:15:00.986621Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:00.986621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Ann Clifton, Azin Ghazimatin, Edgar Tanaka, Edward Ronan, Winstead Zhu","submitted_at":"2025-05-29T18:02:16Z","abstract_excerpt":"Discovering and evaluating long-form talk content such as videos and podcasts poses a significant challenge for users, as it requires a considerable time investment. Previews offer a practical solution by providing concise snippets that showcase key moments of the content, enabling users to make more informed and confident choices. We propose an LLM-based approach for generating podcast episode previews and deploy the solution at scale, serving hundreds of thousands of podcast previews in a real-world application. Comprehensive offline evaluations and online A/B testing demonstrate that LLM-ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.23908","kind":"arxiv","version":2},"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/2505.23908/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.23908","created_at":"2026-07-05T11:15:00.986683+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.23908v2","created_at":"2026-07-05T11:15:00.986683+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.23908","created_at":"2026-07-05T11:15:00.986683+00:00"},{"alias_kind":"pith_short_12","alias_value":"SARUDGRFOJGV","created_at":"2026-07-05T11:15:00.986683+00:00"},{"alias_kind":"pith_short_16","alias_value":"SARUDGRFOJGVYISA","created_at":"2026-07-05T11:15:00.986683+00:00"},{"alias_kind":"pith_short_8","alias_value":"SARUDGRF","created_at":"2026-07-05T11:15:00.986683+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/SARUDGRFOJGVYISA3VEH5HKYX7","json":"https://pith.science/pith/SARUDGRFOJGVYISA3VEH5HKYX7.json","graph_json":"https://pith.science/api/pith-number/SARUDGRFOJGVYISA3VEH5HKYX7/graph.json","events_json":"https://pith.science/api/pith-number/SARUDGRFOJGVYISA3VEH5HKYX7/events.json","paper":"https://pith.science/paper/SARUDGRF"},"agent_actions":{"view_html":"https://pith.science/pith/SARUDGRFOJGVYISA3VEH5HKYX7","download_json":"https://pith.science/pith/SARUDGRFOJGVYISA3VEH5HKYX7.json","view_paper":"https://pith.science/paper/SARUDGRF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.23908&json=true","fetch_graph":"https://pith.science/api/pith-number/SARUDGRFOJGVYISA3VEH5HKYX7/graph.json","fetch_events":"https://pith.science/api/pith-number/SARUDGRFOJGVYISA3VEH5HKYX7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SARUDGRFOJGVYISA3VEH5HKYX7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SARUDGRFOJGVYISA3VEH5HKYX7/action/storage_attestation","attest_author":"https://pith.science/pith/SARUDGRFOJGVYISA3VEH5HKYX7/action/author_attestation","sign_citation":"https://pith.science/pith/SARUDGRFOJGVYISA3VEH5HKYX7/action/citation_signature","submit_replication":"https://pith.science/pith/SARUDGRFOJGVYISA3VEH5HKYX7/action/replication_record"}},"created_at":"2026-07-05T11:15:00.986683+00:00","updated_at":"2026-07-05T11:15:00.986683+00:00"}