{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:R2JGF5AUQCJT3LEMYJIXOUCR7V","short_pith_number":"pith:R2JGF5AU","schema_version":"1.0","canonical_sha256":"8e9262f41480933dac8cc251775051fd56a07ede7c50638e878097ff63726bb0","source":{"kind":"arxiv","id":"2412.20715","version":1},"attestation_state":"computed","paper":{"title":"ChartAdapter: Large Vision-Language Model for Chart Summarization","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.MM","authors_text":"Peixin Xu, Wenqi Fan, Yujuan Ding","submitted_at":"2024-12-30T05:07:34Z","abstract_excerpt":"Chart summarization, which focuses on extracting key information from charts and interpreting it in natural language, is crucial for generating and delivering insights through effective and accessible data analysis. Traditional methods for chart understanding and summarization often rely on multi-stage pipelines, which may produce suboptimal semantic alignment between visual and textual information. In comparison, recently developed LLM-based methods are more dependent on the capability of foundation images or languages, while ignoring the characteristics of chart data and its relevant challen"},"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":"2412.20715","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.MM","submitted_at":"2024-12-30T05:07:34Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"9acb650ce01887a85ed675975d156c3149484053ae174ac2d3a46a7df95ca26a","abstract_canon_sha256":"f561b4e0799f4c89c3cee91b355d2a98e79bc2c9f4d3e3168a8fa6254edf8d99"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:21.265009Z","signature_b64":"/cnDeEXRvrF9lyJk0Sq0D58qyNPTKx2fNQTofFWDCHeR8D8qLb2S9e0EmPcNEKJVaUH3k1xcN1wfW8idYXYCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e9262f41480933dac8cc251775051fd56a07ede7c50638e878097ff63726bb0","last_reissued_at":"2026-07-05T09:55:21.264488Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:21.264488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChartAdapter: Large Vision-Language Model for Chart Summarization","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.MM","authors_text":"Peixin Xu, Wenqi Fan, Yujuan Ding","submitted_at":"2024-12-30T05:07:34Z","abstract_excerpt":"Chart summarization, which focuses on extracting key information from charts and interpreting it in natural language, is crucial for generating and delivering insights through effective and accessible data analysis. Traditional methods for chart understanding and summarization often rely on multi-stage pipelines, which may produce suboptimal semantic alignment between visual and textual information. In comparison, recently developed LLM-based methods are more dependent on the capability of foundation images or languages, while ignoring the characteristics of chart data and its relevant challen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20715","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/2412.20715/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":"2412.20715","created_at":"2026-07-05T09:55:21.264555+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.20715v1","created_at":"2026-07-05T09:55:21.264555+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20715","created_at":"2026-07-05T09:55:21.264555+00:00"},{"alias_kind":"pith_short_12","alias_value":"R2JGF5AUQCJT","created_at":"2026-07-05T09:55:21.264555+00:00"},{"alias_kind":"pith_short_16","alias_value":"R2JGF5AUQCJT3LEM","created_at":"2026-07-05T09:55:21.264555+00:00"},{"alias_kind":"pith_short_8","alias_value":"R2JGF5AU","created_at":"2026-07-05T09:55:21.264555+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02794","citing_title":"CharTool: Tool-Integrated Visual Reasoning for Chart Understanding","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V","json":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V.json","graph_json":"https://pith.science/api/pith-number/R2JGF5AUQCJT3LEMYJIXOUCR7V/graph.json","events_json":"https://pith.science/api/pith-number/R2JGF5AUQCJT3LEMYJIXOUCR7V/events.json","paper":"https://pith.science/paper/R2JGF5AU"},"agent_actions":{"view_html":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V","download_json":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V.json","view_paper":"https://pith.science/paper/R2JGF5AU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.20715&json=true","fetch_graph":"https://pith.science/api/pith-number/R2JGF5AUQCJT3LEMYJIXOUCR7V/graph.json","fetch_events":"https://pith.science/api/pith-number/R2JGF5AUQCJT3LEMYJIXOUCR7V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V/action/storage_attestation","attest_author":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V/action/author_attestation","sign_citation":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V/action/citation_signature","submit_replication":"https://pith.science/pith/R2JGF5AUQCJT3LEMYJIXOUCR7V/action/replication_record"}},"created_at":"2026-07-05T09:55:21.264555+00:00","updated_at":"2026-07-05T09:55:21.264555+00:00"}