{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:7IOOPHL3M4WMQLT2UAKOMRY6FP","short_pith_number":"pith:7IOOPHL3","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"},"canonical_sha256":"fa1ce79d7b672cc82e7aa014e6471e2bd1fcc17c79da1b50a1eedee4a51bbd0f","source":{"kind":"arxiv","id":"1704.01792","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.01792","created_at":"2026-05-18T00:46:11Z"},{"alias_kind":"arxiv_version","alias_value":"1704.01792v3","created_at":"2026-05-18T00:46:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.01792","created_at":"2026-05-18T00:46:11Z"},{"alias_kind":"pith_short_12","alias_value":"7IOOPHL3M4WM","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7IOOPHL3M4WMQLT2","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7IOOPHL3","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:7IOOPHL3M4WMQLT2UAKOMRY6FP","target":"record","payload":{"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"},"canonical_sha256":"fa1ce79d7b672cc82e7aa014e6471e2bd1fcc17c79da1b50a1eedee4a51bbd0f","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"},"source_kind":"arxiv","source_id":"1704.01792","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:46:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WOkZMU81MmFIwg9oyowDQQJtFRDT8pbj/gMycCOb0MnFXSCWhxXSRfmbIjWdne9c8GCHNF57vtU6+seoRwjqDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T13:46:41.946690Z"},"content_sha256":"11ca2d777f3924d96b1788b18cce4a5712c21cd1249c265e942c55476226da9e","schema_version":"1.0","event_id":"sha256:11ca2d777f3924d96b1788b18cce4a5712c21cd1249c265e942c55476226da9e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:7IOOPHL3M4WMQLT2UAKOMRY6FP","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:46:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LcZp5F8vTByn/ehMrsSp7bJi05G/HNhSAmunGQt69Og6YLuYVBGw8vGqGg36OjnrQIT3xB5S7sLHGk5RNGevCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T13:46:41.947083Z"},"content_sha256":"d703031eb3940ba787e0a2742133f6a78815f42fa19531fe5aba4cdc2c655314","schema_version":"1.0","event_id":"sha256:d703031eb3940ba787e0a2742133f6a78815f42fa19531fe5aba4cdc2c655314"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/bundle.json","state_url":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T13:46:41Z","links":{"resolver":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP","bundle":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/bundle.json","state":"https://pith.science/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7IOOPHL3M4WMQLT2UAKOMRY6FP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:7IOOPHL3M4WMQLT2UAKOMRY6FP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0fe3eca712e02d3eeb47ff7da7c248b0028f891064e4d12e030f8e6ce3aacb16","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-06T11:44:07Z","title_canon_sha256":"dcc10893226df3556dcf98f41d2c4b08be26c293248e689793ad89faabd3538d"},"schema_version":"1.0","source":{"id":"1704.01792","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.01792","created_at":"2026-05-18T00:46:11Z"},{"alias_kind":"arxiv_version","alias_value":"1704.01792v3","created_at":"2026-05-18T00:46:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.01792","created_at":"2026-05-18T00:46:11Z"},{"alias_kind":"pith_short_12","alias_value":"7IOOPHL3M4WM","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7IOOPHL3M4WMQLT2","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7IOOPHL3","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:d703031eb3940ba787e0a2742133f6a78815f42fa19531fe5aba4cdc2c655314","target":"graph","created_at":"2026-05-18T00:46:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"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 ","authors_text":"Chuanqi Tan, Furu Wei, Hangbo Bao, Ming Zhou, Nan Yang, Qingyu Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-06T11:44:07Z","title":"Neural Question Generation from Text: A Preliminary Study"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.01792","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:11ca2d777f3924d96b1788b18cce4a5712c21cd1249c265e942c55476226da9e","target":"record","created_at":"2026-05-18T00:46:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0fe3eca712e02d3eeb47ff7da7c248b0028f891064e4d12e030f8e6ce3aacb16","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-06T11:44:07Z","title_canon_sha256":"dcc10893226df3556dcf98f41d2c4b08be26c293248e689793ad89faabd3538d"},"schema_version":"1.0","source":{"id":"1704.01792","kind":"arxiv","version":3}},"canonical_sha256":"fa1ce79d7b672cc82e7aa014e6471e2bd1fcc17c79da1b50a1eedee4a51bbd0f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fa1ce79d7b672cc82e7aa014e6471e2bd1fcc17c79da1b50a1eedee4a51bbd0f","first_computed_at":"2026-05-18T00:46:11.997356Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:46:11.997356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qKYRjCLbw/0pXLc2lyeIteYcSeZ+DdPjbMqHiGJhMlP/cfcOhSaBCPxx/oRIW+xWO6ihFv6hO1+sFWRacukmDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:46:11.997874Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.01792","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11ca2d777f3924d96b1788b18cce4a5712c21cd1249c265e942c55476226da9e","sha256:d703031eb3940ba787e0a2742133f6a78815f42fa19531fe5aba4cdc2c655314"],"state_sha256":"8778cfafea2288e28353e3cce82be9b0fbdab6301f27ae31f620cb1d92bd1da9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LNFfIKWSYhiiWR8Ixzr86OvrgLW712hMzvhoxbzyDNS0ZPMkNKCKst3uWDJkiU17QCjtHB6VmNer1lg5ncf2Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T13:46:41.949046Z","bundle_sha256":"c82bb51bf4cb8466fcec577be4cc1336af2b27d20108081de74c3268566ba821"}}