{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:BAL3XP36E6FS5IIIO6MUKAVJVW","short_pith_number":"pith:BAL3XP36","schema_version":"1.0","canonical_sha256":"0817bbbf7e278b2ea10877994502a9ada44c40a9b52d722fa63691a97fffc424","source":{"kind":"arxiv","id":"2110.06446","version":1},"attestation_state":"computed","paper":{"title":"Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ante Wang, Degen Huang, Fandong Meng, Jiali Zeng, Jie Zhou, Jinsong Su, Junfeng Yao, Shaopeng Lai, Yubin Ge","submitted_at":"2021-10-13T02:18:16Z","abstract_excerpt":"Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering. Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences. Then, in an iterative manner, based on the g"},"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":"2110.06446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-13T02:18:16Z","cross_cats_sorted":[],"title_canon_sha256":"a0e45ab2df5c3679d1a94d9788ee38cf002905c21219d8fb7bfe86117e75192e","abstract_canon_sha256":"fa02e2e7c2774ebb521c095f250e7831f5896645bb37f883611a86784fd3491f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:22:26.066861Z","signature_b64":"QiEB2UulcHW4zbrfhvoarm2jywbv3K/ztzlWa7TlZxfVCHsRDLbUMz8k75iXsok85d5ire1JKyY1Zus4cEllDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0817bbbf7e278b2ea10877994502a9ada44c40a9b52d722fa63691a97fffc424","last_reissued_at":"2026-07-05T03:22:26.066518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:22:26.066518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ante Wang, Degen Huang, Fandong Meng, Jiali Zeng, Jie Zhou, Jinsong Su, Junfeng Yao, Shaopeng Lai, Yubin Ge","submitted_at":"2021-10-13T02:18:16Z","abstract_excerpt":"Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering. Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences. Then, in an iterative manner, based on the g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.06446","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/2110.06446/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":"2110.06446","created_at":"2026-07-05T03:22:26.066573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.06446v1","created_at":"2026-07-05T03:22:26.066573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.06446","created_at":"2026-07-05T03:22:26.066573+00:00"},{"alias_kind":"pith_short_12","alias_value":"BAL3XP36E6FS","created_at":"2026-07-05T03:22:26.066573+00:00"},{"alias_kind":"pith_short_16","alias_value":"BAL3XP36E6FS5III","created_at":"2026-07-05T03:22:26.066573+00:00"},{"alias_kind":"pith_short_8","alias_value":"BAL3XP36","created_at":"2026-07-05T03:22:26.066573+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/BAL3XP36E6FS5IIIO6MUKAVJVW","json":"https://pith.science/pith/BAL3XP36E6FS5IIIO6MUKAVJVW.json","graph_json":"https://pith.science/api/pith-number/BAL3XP36E6FS5IIIO6MUKAVJVW/graph.json","events_json":"https://pith.science/api/pith-number/BAL3XP36E6FS5IIIO6MUKAVJVW/events.json","paper":"https://pith.science/paper/BAL3XP36"},"agent_actions":{"view_html":"https://pith.science/pith/BAL3XP36E6FS5IIIO6MUKAVJVW","download_json":"https://pith.science/pith/BAL3XP36E6FS5IIIO6MUKAVJVW.json","view_paper":"https://pith.science/paper/BAL3XP36","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.06446&json=true","fetch_graph":"https://pith.science/api/pith-number/BAL3XP36E6FS5IIIO6MUKAVJVW/graph.json","fetch_events":"https://pith.science/api/pith-number/BAL3XP36E6FS5IIIO6MUKAVJVW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BAL3XP36E6FS5IIIO6MUKAVJVW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BAL3XP36E6FS5IIIO6MUKAVJVW/action/storage_attestation","attest_author":"https://pith.science/pith/BAL3XP36E6FS5IIIO6MUKAVJVW/action/author_attestation","sign_citation":"https://pith.science/pith/BAL3XP36E6FS5IIIO6MUKAVJVW/action/citation_signature","submit_replication":"https://pith.science/pith/BAL3XP36E6FS5IIIO6MUKAVJVW/action/replication_record"}},"created_at":"2026-07-05T03:22:26.066573+00:00","updated_at":"2026-07-05T03:22:26.066573+00:00"}