{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6WFKWCKBCIVK6PRY26GQPH3W66","short_pith_number":"pith:6WFKWCKB","schema_version":"1.0","canonical_sha256":"f58aab0941122aaf3e38d78d079f76f798393050e1c4bc1de34b262370ffa20c","source":{"kind":"arxiv","id":"2502.14820","version":1},"attestation_state":"computed","paper":{"title":"eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DB","cs.HC"],"primary_cat":"cs.CL","authors_text":"Cristian L\\'opez, Davood Rafiei, Luis Antonio Guti\\'errez Guanilo, Mir Tafseer Nayeem","submitted_at":"2025-02-20T18:41:48Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated exceptional versatility across diverse domains, yet their application in e-commerce remains underexplored due to a lack of domain-specific datasets. To address this gap, we introduce eC-Tab2Text, a novel dataset designed to capture the intricacies of e-commerce, including detailed product attributes and user-specific queries. Leveraging eC-Tab2Text, we focus on text generation from product tables, enabling LLMs to produce high-quality, attribute-specific product reviews from structured tabular data. Fine-tuned models were rigorously evaluated usin"},"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":"2502.14820","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-20T18:41:48Z","cross_cats_sorted":["cs.AI","cs.DB","cs.HC"],"title_canon_sha256":"271e2a10f798c5678af6b0aa48c630a6f78b47a93f3641c5f0743eb8eb673529","abstract_canon_sha256":"8309f8859455d02765831c2da813cd99fbf4dcc3574134c59fb89f9dbbaec891"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:33.041376Z","signature_b64":"1x7u+vOWJ9ej+0BPIkHswF/X4Frs7RVtGNstxAdAsMWRqdkjXENKztEVHKQknZ3JawRZE3cqPWI3aGCOn5YlAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f58aab0941122aaf3e38d78d079f76f798393050e1c4bc1de34b262370ffa20c","last_reissued_at":"2026-07-05T10:17:33.040843Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:33.040843Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DB","cs.HC"],"primary_cat":"cs.CL","authors_text":"Cristian L\\'opez, Davood Rafiei, Luis Antonio Guti\\'errez Guanilo, Mir Tafseer Nayeem","submitted_at":"2025-02-20T18:41:48Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated exceptional versatility across diverse domains, yet their application in e-commerce remains underexplored due to a lack of domain-specific datasets. To address this gap, we introduce eC-Tab2Text, a novel dataset designed to capture the intricacies of e-commerce, including detailed product attributes and user-specific queries. Leveraging eC-Tab2Text, we focus on text generation from product tables, enabling LLMs to produce high-quality, attribute-specific product reviews from structured tabular data. Fine-tuned models were rigorously evaluated usin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14820","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/2502.14820/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":"2502.14820","created_at":"2026-07-05T10:17:33.040898+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.14820v1","created_at":"2026-07-05T10:17:33.040898+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14820","created_at":"2026-07-05T10:17:33.040898+00:00"},{"alias_kind":"pith_short_12","alias_value":"6WFKWCKBCIVK","created_at":"2026-07-05T10:17:33.040898+00:00"},{"alias_kind":"pith_short_16","alias_value":"6WFKWCKBCIVK6PRY","created_at":"2026-07-05T10:17:33.040898+00:00"},{"alias_kind":"pith_short_8","alias_value":"6WFKWCKB","created_at":"2026-07-05T10:17:33.040898+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.02401","citing_title":"Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66","json":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66.json","graph_json":"https://pith.science/api/pith-number/6WFKWCKBCIVK6PRY26GQPH3W66/graph.json","events_json":"https://pith.science/api/pith-number/6WFKWCKBCIVK6PRY26GQPH3W66/events.json","paper":"https://pith.science/paper/6WFKWCKB"},"agent_actions":{"view_html":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66","download_json":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66.json","view_paper":"https://pith.science/paper/6WFKWCKB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.14820&json=true","fetch_graph":"https://pith.science/api/pith-number/6WFKWCKBCIVK6PRY26GQPH3W66/graph.json","fetch_events":"https://pith.science/api/pith-number/6WFKWCKBCIVK6PRY26GQPH3W66/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66/action/storage_attestation","attest_author":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66/action/author_attestation","sign_citation":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66/action/citation_signature","submit_replication":"https://pith.science/pith/6WFKWCKBCIVK6PRY26GQPH3W66/action/replication_record"}},"created_at":"2026-07-05T10:17:33.040898+00:00","updated_at":"2026-07-05T10:17:33.040898+00:00"}