{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:MVZDPNMYVRVTNIIZFW4CFPOGPQ","short_pith_number":"pith:MVZDPNMY","schema_version":"1.0","canonical_sha256":"657237b598ac6b36a1192db822bdc67c31583a036cd657435fb2f36dc24bcb24","source":{"kind":"arxiv","id":"2212.09873","version":2},"attestation_state":"computed","paper":{"title":"A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dongyeop Kang, Karin de Langis","submitted_at":"2022-12-19T21:50:36Z","abstract_excerpt":"There is growing interest in incorporating eye-tracking data and other implicit measures of human language processing into natural language processing (NLP) pipelines. The data from human language processing contain unique insight into human linguistic understanding that could be exploited by language models. However, many unanswered questions remain about the nature of this data and how it can best be utilized in downstream NLP tasks. In this paper, we present eyeStyliency, an eye-tracking dataset for human processing of stylistic text (e.g., politeness). We develop a variety of methods to de"},"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":"2212.09873","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T21:50:36Z","cross_cats_sorted":[],"title_canon_sha256":"4ed71d8934cefce7f534022cd963b07004f311e2e363923894cf2b8f9f4aedfe","abstract_canon_sha256":"7601d75afd233ee5be232ff29e0238702a0d1c929cc8f6d5012c75f496889ed0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:03:19.938541Z","signature_b64":"VI5GgHg6SnOGJd6OCSwuz3XlHQ/faVmeVIrnHpP7uOG8ye7xSa2EGOoeDyrjwx7ny1WVDJBp9tMthE5oSr9dCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"657237b598ac6b36a1192db822bdc67c31583a036cd657435fb2f36dc24bcb24","last_reissued_at":"2026-07-05T07:03:19.937972Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:03:19.937972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dongyeop Kang, Karin de Langis","submitted_at":"2022-12-19T21:50:36Z","abstract_excerpt":"There is growing interest in incorporating eye-tracking data and other implicit measures of human language processing into natural language processing (NLP) pipelines. The data from human language processing contain unique insight into human linguistic understanding that could be exploited by language models. However, many unanswered questions remain about the nature of this data and how it can best be utilized in downstream NLP tasks. In this paper, we present eyeStyliency, an eye-tracking dataset for human processing of stylistic text (e.g., politeness). We develop a variety of methods to de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09873","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.09873/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":"2212.09873","created_at":"2026-07-05T07:03:19.938084+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09873v2","created_at":"2026-07-05T07:03:19.938084+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09873","created_at":"2026-07-05T07:03:19.938084+00:00"},{"alias_kind":"pith_short_12","alias_value":"MVZDPNMYVRVT","created_at":"2026-07-05T07:03:19.938084+00:00"},{"alias_kind":"pith_short_16","alias_value":"MVZDPNMYVRVTNIIZ","created_at":"2026-07-05T07:03:19.938084+00:00"},{"alias_kind":"pith_short_8","alias_value":"MVZDPNMY","created_at":"2026-07-05T07:03:19.938084+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.16272","citing_title":"Learning Explainable Dense Reward Shapes via Bayesian Optimization","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ","json":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ.json","graph_json":"https://pith.science/api/pith-number/MVZDPNMYVRVTNIIZFW4CFPOGPQ/graph.json","events_json":"https://pith.science/api/pith-number/MVZDPNMYVRVTNIIZFW4CFPOGPQ/events.json","paper":"https://pith.science/paper/MVZDPNMY"},"agent_actions":{"view_html":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ","download_json":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ.json","view_paper":"https://pith.science/paper/MVZDPNMY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09873&json=true","fetch_graph":"https://pith.science/api/pith-number/MVZDPNMYVRVTNIIZFW4CFPOGPQ/graph.json","fetch_events":"https://pith.science/api/pith-number/MVZDPNMYVRVTNIIZFW4CFPOGPQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ/action/storage_attestation","attest_author":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ/action/author_attestation","sign_citation":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ/action/citation_signature","submit_replication":"https://pith.science/pith/MVZDPNMYVRVTNIIZFW4CFPOGPQ/action/replication_record"}},"created_at":"2026-07-05T07:03:19.938084+00:00","updated_at":"2026-07-05T07:03:19.938084+00:00"}