{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5JMY5CKDV2IQAIMOEQQI3TZO7F","short_pith_number":"pith:5JMY5CKD","schema_version":"1.0","canonical_sha256":"ea598e8943ae9100218e24208dcf2ef9477c58aa7856e3b2297a95fbf363c37f","source":{"kind":"arxiv","id":"1805.11234","version":1},"attestation_state":"computed","paper":{"title":"Table-to-Text: Describing Table Region with Natural Language","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Duyu Tang, Junwei Bao, Ming Zhou, Nan Duan, Tiejun Zhao, Yuanhua Lv, Zhao Yan","submitted_at":"2018-05-29T03:39:35Z","abstract_excerpt":"In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table. To deal with rare words appearing in a table, we develop a flexible copying mechanism that selectively replicates contents from the table in the output sequence. Extensive experiments demonstrate the accuracy of the model and the power of the copying mechanism. On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model imp"},"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":"1805.11234","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-29T03:39:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f18a137bf8c2f93bfedecfaac3d073ef65fc94d8e7081f6755c2a77f2eb8db5a","abstract_canon_sha256":"f917d5b8eb53162410ad16ea89968fab84d74151aec63170c4f56fe96015d2bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:43.415685Z","signature_b64":"QUOs0rLQy1SQXwhENB5bzJcNkHNwkc9AjQfni16j3LoikHl5GoCq5Wiyvwjl5fCYJDDkwMAwDA9ZZD8HUD9LDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea598e8943ae9100218e24208dcf2ef9477c58aa7856e3b2297a95fbf363c37f","last_reissued_at":"2026-05-18T00:14:43.415114Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:43.415114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Table-to-Text: Describing Table Region with Natural Language","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Duyu Tang, Junwei Bao, Ming Zhou, Nan Duan, Tiejun Zhao, Yuanhua Lv, Zhao Yan","submitted_at":"2018-05-29T03:39:35Z","abstract_excerpt":"In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table. To deal with rare words appearing in a table, we develop a flexible copying mechanism that selectively replicates contents from the table in the output sequence. Extensive experiments demonstrate the accuracy of the model and the power of the copying mechanism. On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model imp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.11234","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":""},"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":"1805.11234","created_at":"2026-05-18T00:14:43.415188+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.11234v1","created_at":"2026-05-18T00:14:43.415188+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.11234","created_at":"2026-05-18T00:14:43.415188+00:00"},{"alias_kind":"pith_short_12","alias_value":"5JMY5CKDV2IQ","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5JMY5CKDV2IQAIMO","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5JMY5CKD","created_at":"2026-05-18T12:32:08.215937+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/5JMY5CKDV2IQAIMOEQQI3TZO7F","json":"https://pith.science/pith/5JMY5CKDV2IQAIMOEQQI3TZO7F.json","graph_json":"https://pith.science/api/pith-number/5JMY5CKDV2IQAIMOEQQI3TZO7F/graph.json","events_json":"https://pith.science/api/pith-number/5JMY5CKDV2IQAIMOEQQI3TZO7F/events.json","paper":"https://pith.science/paper/5JMY5CKD"},"agent_actions":{"view_html":"https://pith.science/pith/5JMY5CKDV2IQAIMOEQQI3TZO7F","download_json":"https://pith.science/pith/5JMY5CKDV2IQAIMOEQQI3TZO7F.json","view_paper":"https://pith.science/paper/5JMY5CKD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.11234&json=true","fetch_graph":"https://pith.science/api/pith-number/5JMY5CKDV2IQAIMOEQQI3TZO7F/graph.json","fetch_events":"https://pith.science/api/pith-number/5JMY5CKDV2IQAIMOEQQI3TZO7F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5JMY5CKDV2IQAIMOEQQI3TZO7F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5JMY5CKDV2IQAIMOEQQI3TZO7F/action/storage_attestation","attest_author":"https://pith.science/pith/5JMY5CKDV2IQAIMOEQQI3TZO7F/action/author_attestation","sign_citation":"https://pith.science/pith/5JMY5CKDV2IQAIMOEQQI3TZO7F/action/citation_signature","submit_replication":"https://pith.science/pith/5JMY5CKDV2IQAIMOEQQI3TZO7F/action/replication_record"}},"created_at":"2026-05-18T00:14:43.415188+00:00","updated_at":"2026-05-18T00:14:43.415188+00:00"}