{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:YN4EIJBWOTPL4PW7VUD67IPMJO","short_pith_number":"pith:YN4EIJBW","canonical_record":{"source":{"id":"2407.00747","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-30T16:12:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"09da81d3da7f2d77ec0095207d9b04939f7cc725b1b522959c8e529b1911f3d6","abstract_canon_sha256":"d81fcaf6304dc7f377570cf26b295df77c2dc1239a996b17e249069ceb628ab9"},"schema_version":"1.0"},"canonical_sha256":"c37844243674debe3edfad07efa1ec4ba569c03609ccd70245b8977f245a3cbd","source":{"kind":"arxiv","id":"2407.00747","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.00747","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"arxiv_version","alias_value":"2407.00747v1","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.00747","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"pith_short_12","alias_value":"YN4EIJBWOTPL","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"pith_short_16","alias_value":"YN4EIJBWOTPL4PW7","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"pith_short_8","alias_value":"YN4EIJBW","created_at":"2026-07-05T08:38:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:YN4EIJBWOTPL4PW7VUD67IPMJO","target":"record","payload":{"canonical_record":{"source":{"id":"2407.00747","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-30T16:12:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"09da81d3da7f2d77ec0095207d9b04939f7cc725b1b522959c8e529b1911f3d6","abstract_canon_sha256":"d81fcaf6304dc7f377570cf26b295df77c2dc1239a996b17e249069ceb628ab9"},"schema_version":"1.0"},"canonical_sha256":"c37844243674debe3edfad07efa1ec4ba569c03609ccd70245b8977f245a3cbd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:24.106915Z","signature_b64":"2tApFKeA8uzU9gjGHFcwBXBmTGqreqYHjFhEXnOrTV7oGY89fdtDV8gbCbNaqv/d45WDVQQ2VArorS/YzYZVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c37844243674debe3edfad07efa1ec4ba569c03609ccd70245b8977f245a3cbd","last_reissued_at":"2026-07-05T08:38:24.106469Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:24.106469Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.00747","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:38:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CkD15gxOple+Pj6IedEjd0Pxb0gjF0qM10HLE/iP9DSzaxz+5YEXqnhqmrIaL4MR6j8Fjn6YHP5yjxTy0O0AAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-27T15:18:39.837461Z"},"content_sha256":"a76780297eb256759b70328d7146cb46aa44386a7f82bc3a18a7ffc38256c166","schema_version":"1.0","event_id":"sha256:a76780297eb256759b70328d7146cb46aa44386a7f82bc3a18a7ffc38256c166"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:YN4EIJBWOTPL4PW7VUD67IPMJO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Comparative Study of Quality Evaluation Methods for Text Summarization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Haihua Chen, Huyen Nguyen, Junhua Ding, Lavanya Pobbathi","submitted_at":"2024-06-30T16:12:37Z","abstract_excerpt":"Evaluating text summarization has been a challenging task in natural language processing (NLP). Automatic metrics which heavily rely on reference summaries are not suitable in many situations, while human evaluation is time-consuming and labor-intensive. To bridge this gap, this paper proposes a novel method based on large language models (LLMs) for evaluating text summarization. We also conducts a comparative study on eight automatic metrics, human evaluation, and our proposed LLM-based method. Seven different types of state-of-the-art (SOTA) summarization models were evaluated. We perform ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.00747","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/2407.00747/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:38:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sLp5DXLeI7kZvbW8g4D+oxusqeTYZEHNYOl9s2N1y3gwllpbOKRItl7ZowM7sp4NB6pneltTwPx7xRzivA0IAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-27T15:18:39.837843Z"},"content_sha256":"1fbaea78dd801494004a6c2db4924783d1a6b0a91c1d015f49be32b73125b9e9","schema_version":"1.0","event_id":"sha256:1fbaea78dd801494004a6c2db4924783d1a6b0a91c1d015f49be32b73125b9e9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YN4EIJBWOTPL4PW7VUD67IPMJO/bundle.json","state_url":"https://pith.science/pith/YN4EIJBWOTPL4PW7VUD67IPMJO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YN4EIJBWOTPL4PW7VUD67IPMJO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-27T15:18:39Z","links":{"resolver":"https://pith.science/pith/YN4EIJBWOTPL4PW7VUD67IPMJO","bundle":"https://pith.science/pith/YN4EIJBWOTPL4PW7VUD67IPMJO/bundle.json","state":"https://pith.science/pith/YN4EIJBWOTPL4PW7VUD67IPMJO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YN4EIJBWOTPL4PW7VUD67IPMJO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YN4EIJBWOTPL4PW7VUD67IPMJO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d81fcaf6304dc7f377570cf26b295df77c2dc1239a996b17e249069ceb628ab9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-30T16:12:37Z","title_canon_sha256":"09da81d3da7f2d77ec0095207d9b04939f7cc725b1b522959c8e529b1911f3d6"},"schema_version":"1.0","source":{"id":"2407.00747","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.00747","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"arxiv_version","alias_value":"2407.00747v1","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.00747","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"pith_short_12","alias_value":"YN4EIJBWOTPL","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"pith_short_16","alias_value":"YN4EIJBWOTPL4PW7","created_at":"2026-07-05T08:38:24Z"},{"alias_kind":"pith_short_8","alias_value":"YN4EIJBW","created_at":"2026-07-05T08:38:24Z"}],"graph_snapshots":[{"event_id":"sha256:1fbaea78dd801494004a6c2db4924783d1a6b0a91c1d015f49be32b73125b9e9","target":"graph","created_at":"2026-07-05T08:38:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2407.00747/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Evaluating text summarization has been a challenging task in natural language processing (NLP). Automatic metrics which heavily rely on reference summaries are not suitable in many situations, while human evaluation is time-consuming and labor-intensive. To bridge this gap, this paper proposes a novel method based on large language models (LLMs) for evaluating text summarization. We also conducts a comparative study on eight automatic metrics, human evaluation, and our proposed LLM-based method. Seven different types of state-of-the-art (SOTA) summarization models were evaluated. We perform ex","authors_text":"Haihua Chen, Huyen Nguyen, Junhua Ding, Lavanya Pobbathi","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-30T16:12:37Z","title":"A Comparative Study of Quality Evaluation Methods for Text Summarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.00747","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a76780297eb256759b70328d7146cb46aa44386a7f82bc3a18a7ffc38256c166","target":"record","created_at":"2026-07-05T08:38:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d81fcaf6304dc7f377570cf26b295df77c2dc1239a996b17e249069ceb628ab9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-30T16:12:37Z","title_canon_sha256":"09da81d3da7f2d77ec0095207d9b04939f7cc725b1b522959c8e529b1911f3d6"},"schema_version":"1.0","source":{"id":"2407.00747","kind":"arxiv","version":1}},"canonical_sha256":"c37844243674debe3edfad07efa1ec4ba569c03609ccd70245b8977f245a3cbd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c37844243674debe3edfad07efa1ec4ba569c03609ccd70245b8977f245a3cbd","first_computed_at":"2026-07-05T08:38:24.106469Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:38:24.106469Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2tApFKeA8uzU9gjGHFcwBXBmTGqreqYHjFhEXnOrTV7oGY89fdtDV8gbCbNaqv/d45WDVQQ2VArorS/YzYZVBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:38:24.106915Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.00747","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a76780297eb256759b70328d7146cb46aa44386a7f82bc3a18a7ffc38256c166","sha256:1fbaea78dd801494004a6c2db4924783d1a6b0a91c1d015f49be32b73125b9e9"],"state_sha256":"8482e14dd52196ddd1d59c8d9d6fc2f1e1b09537678fbee26798d84292bcd4a6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Is5lNv1n5wUwuSvB+G6dwb4u2BblD9v+Lon+KnKY8OdhswxPap9Yz4k+12DwVExhWSaGPK1knnPkmeL3L3FFAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-27T15:18:39.839981Z","bundle_sha256":"805f68e4eed97e327fcf81a7116fd8bf5292c5bf5529b6f6ecd01526efe2d4c0"}}