{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7IOOPHL3M4WMQLT2UAKOMRY6FP","short_pith_number":"pith:7IOOPHL3","schema_version":"1.0","canonical_sha256":"fa1ce79d7b672cc82e7aa014e6471e2bd1fcc17c79da1b50a1eedee4a51bbd0f","source":{"kind":"arxiv","id":"1704.01792","version":3},"attestation_state":"computed","paper":{"title":"Neural Question Generation from Text: A Preliminary Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chuanqi Tan, Furu Wei, Hangbo Bao, Ming Zhou, Nan Yang, Qingyu Zhou","submitted_at":"2017-04-06T11:44:07Z","abstract_excerpt":"Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a "},"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":"1704.01792","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-06T11:44:07Z","cross_cats_sorted":[],"title_canon_sha256":"dcc10893226df3556dcf98f41d2c4b08be26c293248e689793ad89faabd3538d","abstract_canon_sha256":"0fe3eca712e02d3eeb47ff7da7c248b0028f891064e4d12e030f8e6ce3aacb16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:11.997874Z","signature_b64":"qKYRjCLbw/0pXLc2lyeIteYcSeZ+DdPjbMqHiGJhMlP/cfcOhSaBCPxx/oRIW+xWO6ihFv6hO1+sFWRacukmDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa1ce79d7b672cc82e7aa014e6471e2bd1fcc17c79da1b50a1eedee4a51bbd0f","last_reissued_at":"2026-05-18T00:46:11.997356Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:11.997356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Question Generation from Text: A Preliminary Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chuanqi Tan, Furu Wei, Hangbo Bao, Ming Zhou, Nan Yang, Qingyu Zhou","submitted_at":"2017-04-06T11:44:07Z","abstract_excerpt":"Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.01792","kind":"arxiv","version":3},"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":"1704.01792","created_at":"2026-05-18T00:46:11.997438+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.01792v3","created_at":"2026-05-18T00:46:11.997438+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.01792","created_at":"2026-05-18T00:46:11.997438+00:00"},{"alias_kind":"pith_short_12","alias_value":"7IOOPHL3M4WM","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7IOOPHL3M4WMQLT2","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7IOOPHL3","created_at":"2026-05-18T12:31:05.417338+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/7IOOPHL3M4WMQLT2UAKOMRY6FP","json":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP.json","graph_json":"https://pith.science/api/pith-number/7IOOPHL3M4WMQLT2UAKOMRY6FP/graph.json","events_json":"https://pith.science/api/pith-number/7IOOPHL3M4WMQLT2UAKOMRY6FP/events.json","paper":"https://pith.science/paper/7IOOPHL3"},"agent_actions":{"view_html":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP","download_json":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP.json","view_paper":"https://pith.science/paper/7IOOPHL3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.01792&json=true","fetch_graph":"https://pith.science/api/pith-number/7IOOPHL3M4WMQLT2UAKOMRY6FP/graph.json","fetch_events":"https://pith.science/api/pith-number/7IOOPHL3M4WMQLT2UAKOMRY6FP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/action/storage_attestation","attest_author":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/action/author_attestation","sign_citation":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/action/citation_signature","submit_replication":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/action/replication_record"}},"created_at":"2026-05-18T00:46:11.997438+00:00","updated_at":"2026-05-18T00:46:11.997438+00:00"}