{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OYNSHDWSZGUNFRDGXZUABABGTE","short_pith_number":"pith:OYNSHDWS","schema_version":"1.0","canonical_sha256":"761b238ed2c9a8d2c466be68008026990267accb443c1935f9240631a60acb1c","source":{"kind":"arxiv","id":"1704.04368","version":2},"attestation_state":"computed","paper":{"title":"Get To The Point: Summarization with Pointer-Generator Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Abigail See, Christopher D. Manning, Peter J. Liu","submitted_at":"2017-04-14T07:55:19Z","abstract_excerpt":"Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids 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":"1704.04368","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-14T07:55:19Z","cross_cats_sorted":[],"title_canon_sha256":"bb685ef4e9eb4ec2eb385feb3c9f11b59e6af297cf94ac855e8048d9448ed6ef","abstract_canon_sha256":"a4d87da81de687c502e1651efc9ff5ae90e4ce1924173f7b6d1457731baf5a78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:38.506286Z","signature_b64":"8EvKILim+W5yTc9th6OdZLiMeYRPCEcGg68NLf5XBbwH8Tq1BA+6xljHN8lrly2rDYPRD+tCljbNkAO1gtDhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"761b238ed2c9a8d2c466be68008026990267accb443c1935f9240631a60acb1c","last_reissued_at":"2026-05-18T00:45:38.505696Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:38.505696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Get To The Point: Summarization with Pointer-Generator Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Abigail See, Christopher D. Manning, Peter J. Liu","submitted_at":"2017-04-14T07:55:19Z","abstract_excerpt":"Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.04368","kind":"arxiv","version":2},"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":"1704.04368","created_at":"2026-05-18T00:45:38.505793+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.04368v2","created_at":"2026-05-18T00:45:38.505793+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.04368","created_at":"2026-05-18T00:45:38.505793+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYNSHDWSZGUN","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYNSHDWSZGUNFRDG","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYNSHDWS","created_at":"2026-05-18T12:31:34.259226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":6,"sample":[{"citing_arxiv_id":"1907.00570","citing_title":"Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"1907.05664","citing_title":"Saliency Maps Generation for Automatic Text Summarization","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"1907.06330","citing_title":"Ranking sentences from product description & bullets for better search","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2109.10616","citing_title":"Enriching and Controlling Global Semantics for Text Summarization","ref_index":31,"is_internal_anchor":true},{"citing_arxiv_id":"2009.01325","citing_title":"Learning to summarize from human feedback","ref_index":59,"is_internal_anchor":true},{"citing_arxiv_id":"2110.08207","citing_title":"Multitask Prompted Training Enables Zero-Shot Task Generalization","ref_index":58,"is_internal_anchor":true},{"citing_arxiv_id":"1910.13461","citing_title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"1910.10683","citing_title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01311","citing_title":"The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"1909.08593","citing_title":"Fine-Tuning Language Models from Human Preferences","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE","json":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE.json","graph_json":"https://pith.science/api/pith-number/OYNSHDWSZGUNFRDGXZUABABGTE/graph.json","events_json":"https://pith.science/api/pith-number/OYNSHDWSZGUNFRDGXZUABABGTE/events.json","paper":"https://pith.science/paper/OYNSHDWS"},"agent_actions":{"view_html":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE","download_json":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE.json","view_paper":"https://pith.science/paper/OYNSHDWS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.04368&json=true","fetch_graph":"https://pith.science/api/pith-number/OYNSHDWSZGUNFRDGXZUABABGTE/graph.json","fetch_events":"https://pith.science/api/pith-number/OYNSHDWSZGUNFRDGXZUABABGTE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE/action/storage_attestation","attest_author":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE/action/author_attestation","sign_citation":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE/action/citation_signature","submit_replication":"https://pith.science/pith/OYNSHDWSZGUNFRDGXZUABABGTE/action/replication_record"}},"created_at":"2026-05-18T00:45:38.505793+00:00","updated_at":"2026-05-18T00:45:38.505793+00:00"}