{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FCCGDHQ5WOGCHJHJ72ML5VQLS3","short_pith_number":"pith:FCCGDHQ5","schema_version":"1.0","canonical_sha256":"2884619e1db38c23a4e9fe98bed60b96e1ea9116ed058c04023671b8a87ab79f","source":{"kind":"arxiv","id":"2406.03151","version":3},"attestation_state":"computed","paper":{"title":"Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Goran Nenadic, Hao Li, Iqra Zahid, Jiayan Zeng, Riza Batista-Navarro, Tharindu Madusanka, Viktor Schlegel, Xiaochi Wang, Xinran He, Yizhi Li, Yuping Wu","submitted_at":"2024-06-05T11:15:45Z","abstract_excerpt":"With the recent advances of large language models (LLMs), it is no longer infeasible to build an automated debate system that helps people to synthesise persuasive arguments. Previous work attempted this task by integrating multiple components. In our work, we introduce an argument mining dataset that captures the end-to-end process of preparing an argumentative essay for a debate, which covers the tasks of claim and evidence identification (Task 1 ED), evidence convincingness ranking (Task 2 ECR), argumentative essay summarisation and human preference ranking (Task 3 ASR) and metric learning "},"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":"2406.03151","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-05T11:15:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1065121b7b039d1126946bbda174dab22e587c828a5ebc41f8853ad8105b1505","abstract_canon_sha256":"0434ba3a25b5db6a306fd94413feb42500a1b82e2187e15ebe47ab10c79100c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:04.693828Z","signature_b64":"fhhtxm+0bev+c4jphNnhaTQmvAYS2vkXuXvVBbiIgvW6VkStHet9HbpIhxGTrexPCV5ebbgDY++atqtDixM8Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2884619e1db38c23a4e9fe98bed60b96e1ea9116ed058c04023671b8a87ab79f","last_reissued_at":"2026-07-05T08:57:04.693279Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:04.693279Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Goran Nenadic, Hao Li, Iqra Zahid, Jiayan Zeng, Riza Batista-Navarro, Tharindu Madusanka, Viktor Schlegel, Xiaochi Wang, Xinran He, Yizhi Li, Yuping Wu","submitted_at":"2024-06-05T11:15:45Z","abstract_excerpt":"With the recent advances of large language models (LLMs), it is no longer infeasible to build an automated debate system that helps people to synthesise persuasive arguments. Previous work attempted this task by integrating multiple components. In our work, we introduce an argument mining dataset that captures the end-to-end process of preparing an argumentative essay for a debate, which covers the tasks of claim and evidence identification (Task 1 ED), evidence convincingness ranking (Task 2 ECR), argumentative essay summarisation and human preference ranking (Task 3 ASR) and metric learning "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.03151","kind":"arxiv","version":3},"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/2406.03151/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":"2406.03151","created_at":"2026-07-05T08:57:04.693346+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.03151v3","created_at":"2026-07-05T08:57:04.693346+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.03151","created_at":"2026-07-05T08:57:04.693346+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCCGDHQ5WOGC","created_at":"2026-07-05T08:57:04.693346+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCCGDHQ5WOGCHJHJ","created_at":"2026-07-05T08:57:04.693346+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCCGDHQ5","created_at":"2026-07-05T08:57:04.693346+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.19580","citing_title":"ArgCMV: An Argument Summarization Benchmark for the LLM-era","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3","json":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3.json","graph_json":"https://pith.science/api/pith-number/FCCGDHQ5WOGCHJHJ72ML5VQLS3/graph.json","events_json":"https://pith.science/api/pith-number/FCCGDHQ5WOGCHJHJ72ML5VQLS3/events.json","paper":"https://pith.science/paper/FCCGDHQ5"},"agent_actions":{"view_html":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3","download_json":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3.json","view_paper":"https://pith.science/paper/FCCGDHQ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.03151&json=true","fetch_graph":"https://pith.science/api/pith-number/FCCGDHQ5WOGCHJHJ72ML5VQLS3/graph.json","fetch_events":"https://pith.science/api/pith-number/FCCGDHQ5WOGCHJHJ72ML5VQLS3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3/action/storage_attestation","attest_author":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3/action/author_attestation","sign_citation":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3/action/citation_signature","submit_replication":"https://pith.science/pith/FCCGDHQ5WOGCHJHJ72ML5VQLS3/action/replication_record"}},"created_at":"2026-07-05T08:57:04.693346+00:00","updated_at":"2026-07-05T08:57:04.693346+00:00"}