{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NCLUIG23NQAVWCZDPSZRDRJAOW","short_pith_number":"pith:NCLUIG23","schema_version":"1.0","canonical_sha256":"6897441b5b6c015b0b237cb311c52075938f573f60b7f8f5391f0d2a325e77de","source":{"kind":"arxiv","id":"2104.09061","version":1},"attestation_state":"computed","paper":{"title":"Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dan Roth, Fan Zhang, Kazoo Sone, Sihao Chen","submitted_at":"2021-04-19T05:39:24Z","abstract_excerpt":"Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context. To address the issue, we study contrast candidate generation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. We learn a discriminative correction model by generating alternative candidate summaries where named entities and quantities in the generated summary are replaced with o"},"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":"2104.09061","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-19T05:39:24Z","cross_cats_sorted":[],"title_canon_sha256":"0c8f5c7161f86409c1aefec1200c37b07eb403e778c28bc973aada97f0190088","abstract_canon_sha256":"ee3c3b26487491501aabcb19b41253f93003902f5a503dbb9ab4cf66f2afa66b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:32:58.583128Z","signature_b64":"WJv7GJ0MD6+lrxKy2yz0cJ2c+kj/ObYCGhXqtGIKmGXyrRRzJVYKr+KllIB9glkpumecN8NTEl1sNr1GR0IRBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6897441b5b6c015b0b237cb311c52075938f573f60b7f8f5391f0d2a325e77de","last_reissued_at":"2026-07-05T02:32:58.582579Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:32:58.582579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dan Roth, Fan Zhang, Kazoo Sone, Sihao Chen","submitted_at":"2021-04-19T05:39:24Z","abstract_excerpt":"Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context. To address the issue, we study contrast candidate generation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. We learn a discriminative correction model by generating alternative candidate summaries where named entities and quantities in the generated summary are replaced with o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.09061","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/2104.09061/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":"2104.09061","created_at":"2026-07-05T02:32:58.582648+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.09061v1","created_at":"2026-07-05T02:32:58.582648+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.09061","created_at":"2026-07-05T02:32:58.582648+00:00"},{"alias_kind":"pith_short_12","alias_value":"NCLUIG23NQAV","created_at":"2026-07-05T02:32:58.582648+00:00"},{"alias_kind":"pith_short_16","alias_value":"NCLUIG23NQAVWCZD","created_at":"2026-07-05T02:32:58.582648+00:00"},{"alias_kind":"pith_short_8","alias_value":"NCLUIG23","created_at":"2026-07-05T02:32:58.582648+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2308.05374","citing_title":"Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment","ref_index":77,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW","json":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW.json","graph_json":"https://pith.science/api/pith-number/NCLUIG23NQAVWCZDPSZRDRJAOW/graph.json","events_json":"https://pith.science/api/pith-number/NCLUIG23NQAVWCZDPSZRDRJAOW/events.json","paper":"https://pith.science/paper/NCLUIG23"},"agent_actions":{"view_html":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW","download_json":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW.json","view_paper":"https://pith.science/paper/NCLUIG23","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.09061&json=true","fetch_graph":"https://pith.science/api/pith-number/NCLUIG23NQAVWCZDPSZRDRJAOW/graph.json","fetch_events":"https://pith.science/api/pith-number/NCLUIG23NQAVWCZDPSZRDRJAOW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW/action/storage_attestation","attest_author":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW/action/author_attestation","sign_citation":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW/action/citation_signature","submit_replication":"https://pith.science/pith/NCLUIG23NQAVWCZDPSZRDRJAOW/action/replication_record"}},"created_at":"2026-07-05T02:32:58.582648+00:00","updated_at":"2026-07-05T02:32:58.582648+00:00"}