{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:C6HF3TWEOFLW6VKJSFZF4U5DVL","short_pith_number":"pith:C6HF3TWE","schema_version":"1.0","canonical_sha256":"178e5dcec471576f554991725e53a3aad1416a10df8fa6b2dc263e29110e90fa","source":{"kind":"arxiv","id":"1904.08455","version":3},"attestation_state":"computed","paper":{"title":"Headline Generation: Learning from Decomposable Document Titles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"John Bohannon, Oleg Vasilyev, Tom Grek","submitted_at":"2019-04-17T19:03:07Z","abstract_excerpt":"We propose a novel method for generating titles for unstructured text documents. We reframe the problem as a sequential question-answering task. A deep neural network is trained on document-title pairs with decomposable titles, meaning that the vocabulary of the title is a subset of the vocabulary of the document. To train the model we use a corpus of millions of publicly available document-title pairs: news articles and headlines. We present the results of a randomized double-blind trial in which subjects were unaware of which titles were human or machine-generated. When trained on approximat"},"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":"1904.08455","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-17T19:03:07Z","cross_cats_sorted":[],"title_canon_sha256":"4e5ccaf42dcd6201f377022a7c9ea2361d5d1141702785d9096f37f98472b8ea","abstract_canon_sha256":"867e0ff9798ecdcfabd1bc510848a9a306e9fccb0090e0d120720d2471942c7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:35.710260Z","signature_b64":"JhN3DyEGD54+mskL9tq60ndX4fCb1+hR+yrhW0YHhvACC/9BG9OYm3ZKQoXSGtRHI9C7D7SpGrq2jZHAPAIZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"178e5dcec471576f554991725e53a3aad1416a10df8fa6b2dc263e29110e90fa","last_reissued_at":"2026-05-17T23:46:35.709540Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:35.709540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Headline Generation: Learning from Decomposable Document Titles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"John Bohannon, Oleg Vasilyev, Tom Grek","submitted_at":"2019-04-17T19:03:07Z","abstract_excerpt":"We propose a novel method for generating titles for unstructured text documents. We reframe the problem as a sequential question-answering task. A deep neural network is trained on document-title pairs with decomposable titles, meaning that the vocabulary of the title is a subset of the vocabulary of the document. To train the model we use a corpus of millions of publicly available document-title pairs: news articles and headlines. We present the results of a randomized double-blind trial in which subjects were unaware of which titles were human or machine-generated. When trained on approximat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08455","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":"1904.08455","created_at":"2026-05-17T23:46:35.709663+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.08455v3","created_at":"2026-05-17T23:46:35.709663+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.08455","created_at":"2026-05-17T23:46:35.709663+00:00"},{"alias_kind":"pith_short_12","alias_value":"C6HF3TWEOFLW","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"C6HF3TWEOFLW6VKJ","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"C6HF3TWE","created_at":"2026-05-18T12:33:12.712433+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/C6HF3TWEOFLW6VKJSFZF4U5DVL","json":"https://pith.science/pith/C6HF3TWEOFLW6VKJSFZF4U5DVL.json","graph_json":"https://pith.science/api/pith-number/C6HF3TWEOFLW6VKJSFZF4U5DVL/graph.json","events_json":"https://pith.science/api/pith-number/C6HF3TWEOFLW6VKJSFZF4U5DVL/events.json","paper":"https://pith.science/paper/C6HF3TWE"},"agent_actions":{"view_html":"https://pith.science/pith/C6HF3TWEOFLW6VKJSFZF4U5DVL","download_json":"https://pith.science/pith/C6HF3TWEOFLW6VKJSFZF4U5DVL.json","view_paper":"https://pith.science/paper/C6HF3TWE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.08455&json=true","fetch_graph":"https://pith.science/api/pith-number/C6HF3TWEOFLW6VKJSFZF4U5DVL/graph.json","fetch_events":"https://pith.science/api/pith-number/C6HF3TWEOFLW6VKJSFZF4U5DVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C6HF3TWEOFLW6VKJSFZF4U5DVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C6HF3TWEOFLW6VKJSFZF4U5DVL/action/storage_attestation","attest_author":"https://pith.science/pith/C6HF3TWEOFLW6VKJSFZF4U5DVL/action/author_attestation","sign_citation":"https://pith.science/pith/C6HF3TWEOFLW6VKJSFZF4U5DVL/action/citation_signature","submit_replication":"https://pith.science/pith/C6HF3TWEOFLW6VKJSFZF4U5DVL/action/replication_record"}},"created_at":"2026-05-17T23:46:35.709663+00:00","updated_at":"2026-05-17T23:46:35.709663+00:00"}