{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HYK4J4422MMHDTG73NEBGBRVC7","short_pith_number":"pith:HYK4J442","schema_version":"1.0","canonical_sha256":"3e15c4f39ad31871ccdfdb4813063517f8942f29cca568127f05ec04b4ebfeb0","source":{"kind":"arxiv","id":"2403.05303","version":1},"attestation_state":"computed","paper":{"title":"ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ines Reinig, Kai Eckert, Simone Paolo Ponzetto, Sotaro Takeshita, Tommaso Green","submitted_at":"2024-03-08T13:32:01Z","abstract_excerpt":"Extensive efforts in the past have been directed toward the development of summarization datasets. However, a predominant number of these resources have been (semi)-automatically generated, typically through web data crawling, resulting in subpar resources for training and evaluating summarization systems, a quality compromise that is arguably due to the substantial costs associated with generating ground-truth summaries, particularly for diverse languages and specialized domains. To address this issue, we present ACLSum, a novel summarization dataset carefully crafted and evaluated by domain "},"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":"2403.05303","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-08T13:32:01Z","cross_cats_sorted":[],"title_canon_sha256":"a137659ac0a6bc1ce2a782c16e197f6656136f22e52913d65fa5c331053d29c1","abstract_canon_sha256":"c09f0b0bd3f2a6c5a57e1d874246933fb86c0a4735369a0e510323b7c3c4f678"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:53:53.407213Z","signature_b64":"HA2T0b1bf6T95WUjeHZyMT+3WXRLdd6gUoYbSjriXs4JcdXcBeQu3WX+4NTol4snlrlfRv29vwVyfIgeyF99Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e15c4f39ad31871ccdfdb4813063517f8942f29cca568127f05ec04b4ebfeb0","last_reissued_at":"2026-07-05T07:53:53.406696Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:53:53.406696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ines Reinig, Kai Eckert, Simone Paolo Ponzetto, Sotaro Takeshita, Tommaso Green","submitted_at":"2024-03-08T13:32:01Z","abstract_excerpt":"Extensive efforts in the past have been directed toward the development of summarization datasets. However, a predominant number of these resources have been (semi)-automatically generated, typically through web data crawling, resulting in subpar resources for training and evaluating summarization systems, a quality compromise that is arguably due to the substantial costs associated with generating ground-truth summaries, particularly for diverse languages and specialized domains. To address this issue, we present ACLSum, a novel summarization dataset carefully crafted and evaluated by domain "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.05303","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/2403.05303/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":"2403.05303","created_at":"2026-07-05T07:53:53.406773+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.05303v1","created_at":"2026-07-05T07:53:53.406773+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.05303","created_at":"2026-07-05T07:53:53.406773+00:00"},{"alias_kind":"pith_short_12","alias_value":"HYK4J4422MMH","created_at":"2026-07-05T07:53:53.406773+00:00"},{"alias_kind":"pith_short_16","alias_value":"HYK4J4422MMHDTG7","created_at":"2026-07-05T07:53:53.406773+00:00"},{"alias_kind":"pith_short_8","alias_value":"HYK4J442","created_at":"2026-07-05T07:53:53.406773+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.01314","citing_title":"Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7","json":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7.json","graph_json":"https://pith.science/api/pith-number/HYK4J4422MMHDTG73NEBGBRVC7/graph.json","events_json":"https://pith.science/api/pith-number/HYK4J4422MMHDTG73NEBGBRVC7/events.json","paper":"https://pith.science/paper/HYK4J442"},"agent_actions":{"view_html":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7","download_json":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7.json","view_paper":"https://pith.science/paper/HYK4J442","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.05303&json=true","fetch_graph":"https://pith.science/api/pith-number/HYK4J4422MMHDTG73NEBGBRVC7/graph.json","fetch_events":"https://pith.science/api/pith-number/HYK4J4422MMHDTG73NEBGBRVC7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7/action/storage_attestation","attest_author":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7/action/author_attestation","sign_citation":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7/action/citation_signature","submit_replication":"https://pith.science/pith/HYK4J4422MMHDTG73NEBGBRVC7/action/replication_record"}},"created_at":"2026-07-05T07:53:53.406773+00:00","updated_at":"2026-07-05T07:53:53.406773+00:00"}