{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6CJ65P4RB4N2IB6KVVOGS5VJ4K","short_pith_number":"pith:6CJ65P4R","schema_version":"1.0","canonical_sha256":"f093eebf910f1ba407caad5c6976a9e28e928cb185e64c28731982dfae5b7850","source":{"kind":"arxiv","id":"2303.03278","version":1},"attestation_state":"computed","paper":{"title":"Faithfulness-Aware Decoding Strategies for Abstractive Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"David Wan, Kathleen McKeown, Markus Dreyer, Mengwen Liu, Mohit Bansal","submitted_at":"2023-03-06T16:49:27Z","abstract_excerpt":"Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied. We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization. We find a consistent trend where beam search with large beam sizes produces the most faithful summaries while nucleus sampling generates the least faithful ones. We propose two faithfulness-aware generation methods to further improve faithfulness over current gene"},"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":"2303.03278","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-03-06T16:49:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"83ecfd0e631fda19279f16c576f1380905e8ff3f5e9ba13af24b59b36543f049","abstract_canon_sha256":"7538b1b9f8bc7b5690aad3fc26f3c1a820d3e8db2ea2dd0d5588045b6e08801c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:48:27.821825Z","signature_b64":"wN+D6SmNPXT8VW+Edl4Vy8uq/J3m7ug5D27L+Vm7FpLc+6rAW40yEg6kbBxdRp7IeL6FLnCwpIPMZsG8smL/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f093eebf910f1ba407caad5c6976a9e28e928cb185e64c28731982dfae5b7850","last_reissued_at":"2026-07-05T05:48:27.821353Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:48:27.821353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Faithfulness-Aware Decoding Strategies for Abstractive Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"David Wan, Kathleen McKeown, Markus Dreyer, Mengwen Liu, Mohit Bansal","submitted_at":"2023-03-06T16:49:27Z","abstract_excerpt":"Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied. We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization. We find a consistent trend where beam search with large beam sizes produces the most faithful summaries while nucleus sampling generates the least faithful ones. We propose two faithfulness-aware generation methods to further improve faithfulness over current gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.03278","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/2303.03278/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":"2303.03278","created_at":"2026-07-05T05:48:27.821421+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.03278v1","created_at":"2026-07-05T05:48:27.821421+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.03278","created_at":"2026-07-05T05:48:27.821421+00:00"},{"alias_kind":"pith_short_12","alias_value":"6CJ65P4RB4N2","created_at":"2026-07-05T05:48:27.821421+00:00"},{"alias_kind":"pith_short_16","alias_value":"6CJ65P4RB4N2IB6K","created_at":"2026-07-05T05:48:27.821421+00:00"},{"alias_kind":"pith_short_8","alias_value":"6CJ65P4R","created_at":"2026-07-05T05:48:27.821421+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12807","citing_title":"Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K","json":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K.json","graph_json":"https://pith.science/api/pith-number/6CJ65P4RB4N2IB6KVVOGS5VJ4K/graph.json","events_json":"https://pith.science/api/pith-number/6CJ65P4RB4N2IB6KVVOGS5VJ4K/events.json","paper":"https://pith.science/paper/6CJ65P4R"},"agent_actions":{"view_html":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K","download_json":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K.json","view_paper":"https://pith.science/paper/6CJ65P4R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.03278&json=true","fetch_graph":"https://pith.science/api/pith-number/6CJ65P4RB4N2IB6KVVOGS5VJ4K/graph.json","fetch_events":"https://pith.science/api/pith-number/6CJ65P4RB4N2IB6KVVOGS5VJ4K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K/action/storage_attestation","attest_author":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K/action/author_attestation","sign_citation":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K/action/citation_signature","submit_replication":"https://pith.science/pith/6CJ65P4RB4N2IB6KVVOGS5VJ4K/action/replication_record"}},"created_at":"2026-07-05T05:48:27.821421+00:00","updated_at":"2026-07-05T05:48:27.821421+00:00"}