{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QK6K3PMICSJDU42NPCUR7GAWJN","short_pith_number":"pith:QK6K3PMI","schema_version":"1.0","canonical_sha256":"82bcadbd8814923a734d78a91f98164b6af66fecf762137a499f7b04c5fb500a","source":{"kind":"arxiv","id":"2104.05938","version":1},"attestation_state":"computed","paper":{"title":"QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ahmad Zaidi, Ahmed Hassan Awadallah, Asli Celikyilmaz, Da Yin, Dragomir Radev, Ming Zhong, Mutethia Mutuma, Rahul Jha, Tao Yu, Xipeng Qiu, Yang Liu","submitted_at":"2021-04-13T05:00:35Z","abstract_excerpt":"Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans"},"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":"2104.05938","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-04-13T05:00:35Z","cross_cats_sorted":[],"title_canon_sha256":"8bb07e2d231b5c38ca8a1f3769d53d7c643206b84ca0f15bd563516b9ef53e3d","abstract_canon_sha256":"ee1fb8ad3ba00ad1984f065dc763694e88be7035a318bc36f2b888b33af05172"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:31:27.260559Z","signature_b64":"qv4UE70QYkavUbCsCwxtKGdwNcvgMN5rOGprFg0IjBHg2w7kEVjZJb8kJLv/AcoU89PSDZEj7h/IKgKFL3Y2Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82bcadbd8814923a734d78a91f98164b6af66fecf762137a499f7b04c5fb500a","last_reissued_at":"2026-07-05T02:31:27.260067Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:31:27.260067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ahmad Zaidi, Ahmed Hassan Awadallah, Asli Celikyilmaz, Da Yin, Dragomir Radev, Ming Zhong, Mutethia Mutuma, Rahul Jha, Tao Yu, Xipeng Qiu, Yang Liu","submitted_at":"2021-04-13T05:00:35Z","abstract_excerpt":"Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.05938","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2104.05938/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":"2104.05938","created_at":"2026-07-05T02:31:27.260139+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.05938v1","created_at":"2026-07-05T02:31:27.260139+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.05938","created_at":"2026-07-05T02:31:27.260139+00:00"},{"alias_kind":"pith_short_12","alias_value":"QK6K3PMICSJD","created_at":"2026-07-05T02:31:27.260139+00:00"},{"alias_kind":"pith_short_16","alias_value":"QK6K3PMICSJDU42N","created_at":"2026-07-05T02:31:27.260139+00:00"},{"alias_kind":"pith_short_8","alias_value":"QK6K3PMI","created_at":"2026-07-05T02:31:27.260139+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29563","citing_title":"Coverage-Driven KV Cache Eviction for Efficient and Improved Inference of LLM","ref_index":87,"is_internal_anchor":false},{"citing_arxiv_id":"2312.10997","citing_title":"Retrieval-Augmented Generation for Large Language Models: A Survey","ref_index":125,"is_internal_anchor":false},{"citing_arxiv_id":"2409.06679","citing_title":"E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2407.11550","citing_title":"Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2404.14469","citing_title":"SnapKV: LLM Knows What You are Looking for Before Generation","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08964","citing_title":"Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents","ref_index":171,"is_internal_anchor":false},{"citing_arxiv_id":"2307.08621","citing_title":"Retentive Network: A Successor to Transformer for Large Language Models","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14930","citing_title":"IE as Cache: Information Extraction Enhanced Agentic Reasoning","ref_index":35,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN","json":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN.json","graph_json":"https://pith.science/api/pith-number/QK6K3PMICSJDU42NPCUR7GAWJN/graph.json","events_json":"https://pith.science/api/pith-number/QK6K3PMICSJDU42NPCUR7GAWJN/events.json","paper":"https://pith.science/paper/QK6K3PMI"},"agent_actions":{"view_html":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN","download_json":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN.json","view_paper":"https://pith.science/paper/QK6K3PMI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.05938&json=true","fetch_graph":"https://pith.science/api/pith-number/QK6K3PMICSJDU42NPCUR7GAWJN/graph.json","fetch_events":"https://pith.science/api/pith-number/QK6K3PMICSJDU42NPCUR7GAWJN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN/action/storage_attestation","attest_author":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN/action/author_attestation","sign_citation":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN/action/citation_signature","submit_replication":"https://pith.science/pith/QK6K3PMICSJDU42NPCUR7GAWJN/action/replication_record"}},"created_at":"2026-07-05T02:31:27.260139+00:00","updated_at":"2026-07-05T02:31:27.260139+00:00"}