{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CQU722AXPRLNAYAWIPQPJUDA7K","short_pith_number":"pith:CQU722AX","schema_version":"1.0","canonical_sha256":"1429fd68177c56d0601643e0f4d060faac37650b0473cb0b2003c4cbe18f761a","source":{"kind":"arxiv","id":"2306.11518","version":2},"attestation_state":"computed","paper":{"title":"One model to rule them all: ranking Slovene summarizers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ale\\v{s} \\v{Z}agar, Marko Robnik-\\v{S}ikonja","submitted_at":"2023-06-20T13:12:58Z","abstract_excerpt":"Text summarization is an essential task in natural language processing, and researchers have developed various approaches over the years, ranging from rule-based systems to neural networks. However, there is no single model or approach that performs well on every type of text. We propose a system that recommends the most suitable summarization model for a given text. The proposed system employs a fully connected neural network that analyzes the input content and predicts which summarizer should score the best in terms of ROUGE score for a given input. The meta-model selects among four differen"},"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":"2306.11518","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-06-20T13:12:58Z","cross_cats_sorted":[],"title_canon_sha256":"c63115e24629fbbd5ccc968b122d6255f689e9066b3ea8d70c087f5ace8fdc0a","abstract_canon_sha256":"528842f7c518b9a2525bb23f0936f4b017363ff9b71d7c0c24197b4cdc7a7930"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:38:01.124796Z","signature_b64":"ymqfPpJBrbbmHr+02j31pObJpzT4qCjI2kEflmrK079ZTPp/8Q6keKP0fc5hHn3HTwnC9/cvaUczKOJZWSgWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1429fd68177c56d0601643e0f4d060faac37650b0473cb0b2003c4cbe18f761a","last_reissued_at":"2026-07-05T06:38:01.124358Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:38:01.124358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One model to rule them all: ranking Slovene summarizers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ale\\v{s} \\v{Z}agar, Marko Robnik-\\v{S}ikonja","submitted_at":"2023-06-20T13:12:58Z","abstract_excerpt":"Text summarization is an essential task in natural language processing, and researchers have developed various approaches over the years, ranging from rule-based systems to neural networks. However, there is no single model or approach that performs well on every type of text. We propose a system that recommends the most suitable summarization model for a given text. The proposed system employs a fully connected neural network that analyzes the input content and predicts which summarizer should score the best in terms of ROUGE score for a given input. The meta-model selects among four differen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11518","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/2306.11518/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":"2306.11518","created_at":"2026-07-05T06:38:01.124431+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.11518v2","created_at":"2026-07-05T06:38:01.124431+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11518","created_at":"2026-07-05T06:38:01.124431+00:00"},{"alias_kind":"pith_short_12","alias_value":"CQU722AXPRLN","created_at":"2026-07-05T06:38:01.124431+00:00"},{"alias_kind":"pith_short_16","alias_value":"CQU722AXPRLNAYAW","created_at":"2026-07-05T06:38:01.124431+00:00"},{"alias_kind":"pith_short_8","alias_value":"CQU722AX","created_at":"2026-07-05T06:38:01.124431+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/CQU722AXPRLNAYAWIPQPJUDA7K","json":"https://pith.science/pith/CQU722AXPRLNAYAWIPQPJUDA7K.json","graph_json":"https://pith.science/api/pith-number/CQU722AXPRLNAYAWIPQPJUDA7K/graph.json","events_json":"https://pith.science/api/pith-number/CQU722AXPRLNAYAWIPQPJUDA7K/events.json","paper":"https://pith.science/paper/CQU722AX"},"agent_actions":{"view_html":"https://pith.science/pith/CQU722AXPRLNAYAWIPQPJUDA7K","download_json":"https://pith.science/pith/CQU722AXPRLNAYAWIPQPJUDA7K.json","view_paper":"https://pith.science/paper/CQU722AX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.11518&json=true","fetch_graph":"https://pith.science/api/pith-number/CQU722AXPRLNAYAWIPQPJUDA7K/graph.json","fetch_events":"https://pith.science/api/pith-number/CQU722AXPRLNAYAWIPQPJUDA7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CQU722AXPRLNAYAWIPQPJUDA7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CQU722AXPRLNAYAWIPQPJUDA7K/action/storage_attestation","attest_author":"https://pith.science/pith/CQU722AXPRLNAYAWIPQPJUDA7K/action/author_attestation","sign_citation":"https://pith.science/pith/CQU722AXPRLNAYAWIPQPJUDA7K/action/citation_signature","submit_replication":"https://pith.science/pith/CQU722AXPRLNAYAWIPQPJUDA7K/action/replication_record"}},"created_at":"2026-07-05T06:38:01.124431+00:00","updated_at":"2026-07-05T06:38:01.124431+00:00"}