{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WYOUPRILAT7OZEKCGT6KV5T5C4","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":"b3feb52f40831f8538e24850db21bc2ee78764dfbd7a6e918d3accb5d6201b72","cross_cats_sorted":["cs.HC"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-27T02:21:19Z","title_canon_sha256":"193aca6b860731bd4b9a6966d2ea5158d3fd0b31ff6ba0b879dbb7e2ba6ee80b"},"schema_version":"1.0","source":{"id":"2203.14187","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14187","created_at":"2026-07-05T09:15:25Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14187v2","created_at":"2026-07-05T09:15:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14187","created_at":"2026-07-05T09:15:25Z"},{"alias_kind":"pith_short_12","alias_value":"WYOUPRILAT7O","created_at":"2026-07-05T09:15:25Z"},{"alias_kind":"pith_short_16","alias_value":"WYOUPRILAT7OZEKC","created_at":"2026-07-05T09:15:25Z"},{"alias_kind":"pith_short_8","alias_value":"WYOUPRIL","created_at":"2026-07-05T09:15:25Z"}],"graph_snapshots":[{"event_id":"sha256:e7bf1e8997af977eba4af7e7501cf46e0f9dfc41cb79c5ab5b2e75baae1f46d5","target":"graph","created_at":"2026-07-05T09:15:25Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2203.14187/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generating educational questions of fairytales or storybooks is vital for improving children's literacy ability. However, it is challenging to generate questions that capture the interesting aspects of a fairytale story with educational meaningfulness. In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. To train the event-centric summarizer, we finetune a pre-trained transformer-based sequence-to-sequence model ","authors_text":"Chengzhong Liu, Dakuo Wang, Mo Yu, Xiaojuan Ma, Yufang Hou, Zhenjie Zhao","cross_cats":["cs.HC"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-27T02:21:19Z","title":"Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14187","kind":"arxiv","version":2},"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":"sha256:01a3aa58e5084ae1ff4ff1a4011e28819e0c632b06df56b7dd4fe2604789fa02","target":"record","created_at":"2026-07-05T09:15:25Z","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":"b3feb52f40831f8538e24850db21bc2ee78764dfbd7a6e918d3accb5d6201b72","cross_cats_sorted":["cs.HC"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-27T02:21:19Z","title_canon_sha256":"193aca6b860731bd4b9a6966d2ea5158d3fd0b31ff6ba0b879dbb7e2ba6ee80b"},"schema_version":"1.0","source":{"id":"2203.14187","kind":"arxiv","version":2}},"canonical_sha256":"b61d47c50b04feec914234fcaaf67d172773a21e27aa49fac2765d4d3d1a1753","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b61d47c50b04feec914234fcaaf67d172773a21e27aa49fac2765d4d3d1a1753","first_computed_at":"2026-07-05T09:15:25.486549Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:15:25.486549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Gl0vegM0PQSNa9eWjCIjpjse2p7AXu3r+AWvagiV54BoTatMNm4/5Pe7CmevDcs6NIVCq8U0sTDS7P27J7kUCA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:15:25.487097Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.14187","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:01a3aa58e5084ae1ff4ff1a4011e28819e0c632b06df56b7dd4fe2604789fa02","sha256:e7bf1e8997af977eba4af7e7501cf46e0f9dfc41cb79c5ab5b2e75baae1f46d5"],"state_sha256":"cecbcc0e8b19d41fc5f3e2c69812a1ed129b646d82c5880ffecc699e890cdd8e"}