{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ERW7XJJT7CF76IMD3JIR5VUXI2","short_pith_number":"pith:ERW7XJJT","schema_version":"1.0","canonical_sha256":"246dfba533f88bff2183da511ed69746965c1462028361e13f57c35d3133c547","source":{"kind":"arxiv","id":"2501.01014","version":1},"attestation_state":"computed","paper":{"title":"MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Changshan Li, Chengze Zhang, Shiyang Gao","submitted_at":"2025-01-02T02:35:38Z","abstract_excerpt":"The exponential growth of data and advancements in big data technologies have created a demand for more efficient and automated approaches to data analysis and storytelling. However, automated data analysis systems still face challenges in leveraging large language models (LLMs) for data insight discovery, augmented analysis, and data storytelling. This paper introduces the Multidimensional Data Storytelling Framework (MDSF) based on large language models for automated insight generation and context-aware storytelling. The framework incorporates advanced preprocessing techniques, augmented ana"},"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":"2501.01014","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-02T02:35:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a58f53040dc6913790bd8561c6a82ec2b5ffc6cf5ae57a5741e4a48660b2f7ce","abstract_canon_sha256":"c8bd9b998cfee3fddebe5aaae8e4406dff8b2cb3c2849cba4621d0ab5fc5c351"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:13.126672Z","signature_b64":"L9orrkNxwCeUg0CTShZ7JCN7Y1J5v/tB8qwMRi71kEDd6xmRybDxvSif9B7ZxazrBsKgvsGCsxqO34DjI/eTAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"246dfba533f88bff2183da511ed69746965c1462028361e13f57c35d3133c547","last_reissued_at":"2026-07-05T09:56:13.126183Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:13.126183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Changshan Li, Chengze Zhang, Shiyang Gao","submitted_at":"2025-01-02T02:35:38Z","abstract_excerpt":"The exponential growth of data and advancements in big data technologies have created a demand for more efficient and automated approaches to data analysis and storytelling. However, automated data analysis systems still face challenges in leveraging large language models (LLMs) for data insight discovery, augmented analysis, and data storytelling. This paper introduces the Multidimensional Data Storytelling Framework (MDSF) based on large language models for automated insight generation and context-aware storytelling. The framework incorporates advanced preprocessing techniques, augmented ana"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01014","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/2501.01014/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":"2501.01014","created_at":"2026-07-05T09:56:13.126242+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.01014v1","created_at":"2026-07-05T09:56:13.126242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01014","created_at":"2026-07-05T09:56:13.126242+00:00"},{"alias_kind":"pith_short_12","alias_value":"ERW7XJJT7CF7","created_at":"2026-07-05T09:56:13.126242+00:00"},{"alias_kind":"pith_short_16","alias_value":"ERW7XJJT7CF76IMD","created_at":"2026-07-05T09:56:13.126242+00:00"},{"alias_kind":"pith_short_8","alias_value":"ERW7XJJT","created_at":"2026-07-05T09:56:13.126242+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/ERW7XJJT7CF76IMD3JIR5VUXI2","json":"https://pith.science/pith/ERW7XJJT7CF76IMD3JIR5VUXI2.json","graph_json":"https://pith.science/api/pith-number/ERW7XJJT7CF76IMD3JIR5VUXI2/graph.json","events_json":"https://pith.science/api/pith-number/ERW7XJJT7CF76IMD3JIR5VUXI2/events.json","paper":"https://pith.science/paper/ERW7XJJT"},"agent_actions":{"view_html":"https://pith.science/pith/ERW7XJJT7CF76IMD3JIR5VUXI2","download_json":"https://pith.science/pith/ERW7XJJT7CF76IMD3JIR5VUXI2.json","view_paper":"https://pith.science/paper/ERW7XJJT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.01014&json=true","fetch_graph":"https://pith.science/api/pith-number/ERW7XJJT7CF76IMD3JIR5VUXI2/graph.json","fetch_events":"https://pith.science/api/pith-number/ERW7XJJT7CF76IMD3JIR5VUXI2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ERW7XJJT7CF76IMD3JIR5VUXI2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ERW7XJJT7CF76IMD3JIR5VUXI2/action/storage_attestation","attest_author":"https://pith.science/pith/ERW7XJJT7CF76IMD3JIR5VUXI2/action/author_attestation","sign_citation":"https://pith.science/pith/ERW7XJJT7CF76IMD3JIR5VUXI2/action/citation_signature","submit_replication":"https://pith.science/pith/ERW7XJJT7CF76IMD3JIR5VUXI2/action/replication_record"}},"created_at":"2026-07-05T09:56:13.126242+00:00","updated_at":"2026-07-05T09:56:13.126242+00:00"}