{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QH7HFXFE6UMXENALHXSMU4NJU6","short_pith_number":"pith:QH7HFXFE","schema_version":"1.0","canonical_sha256":"81fe72dca4f51972340b3de4ca71a9a7b9b56851c7ad467be9c46bbcc88646af","source":{"kind":"arxiv","id":"2310.00785","version":4},"attestation_state":"computed","paper":{"title":"BooookScore: A systematic exploration of book-length summarization in the era of LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Kyle Lo, Mohit Iyyer, Tanya Goyal, Yapei Chang","submitted_at":"2023-10-01T20:46:44Z","abstract_excerpt":"Summarizing book-length documents (>100K tokens) that exceed the context window size of large language models (LLMs) requires first breaking the input document into smaller chunks and then prompting an LLM to merge, update, and compress chunk-level summaries. Despite the complexity and importance of this task, it has yet to be meaningfully studied due to the challenges of evaluation: existing book-length summarization datasets (e.g., BookSum) are in the pretraining data of most public LLMs, and existing evaluation methods struggle to capture errors made by modern LLM summarizers. In this paper"},"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":"2310.00785","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-01T20:46:44Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"6889a57d106c307ecdbe733441d3c2f36b9e5ad998cc8c840c1231be2ae0f987","abstract_canon_sha256":"217c0fd99fad2e57a1b5dd8a6197d970981c7592c6afe8769836e30218f5fdcd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:29.602670Z","signature_b64":"NffnVCmcY1z4vP70pPkuj+pD+Q1tHcZ2qocHpyCgkDPeIup/ukU6bd4b96mjaCaQ4Zw9fQQ7moshiBspu15OAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81fe72dca4f51972340b3de4ca71a9a7b9b56851c7ad467be9c46bbcc88646af","last_reissued_at":"2026-07-05T08:07:29.602177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:29.602177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BooookScore: A systematic exploration of book-length summarization in the era of LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Kyle Lo, Mohit Iyyer, Tanya Goyal, Yapei Chang","submitted_at":"2023-10-01T20:46:44Z","abstract_excerpt":"Summarizing book-length documents (>100K tokens) that exceed the context window size of large language models (LLMs) requires first breaking the input document into smaller chunks and then prompting an LLM to merge, update, and compress chunk-level summaries. Despite the complexity and importance of this task, it has yet to be meaningfully studied due to the challenges of evaluation: existing book-length summarization datasets (e.g., BookSum) are in the pretraining data of most public LLMs, and existing evaluation methods struggle to capture errors made by modern LLM summarizers. In this paper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00785","kind":"arxiv","version":4},"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/2310.00785/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":"2310.00785","created_at":"2026-07-05T08:07:29.602235+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.00785v4","created_at":"2026-07-05T08:07:29.602235+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00785","created_at":"2026-07-05T08:07:29.602235+00:00"},{"alias_kind":"pith_short_12","alias_value":"QH7HFXFE6UMX","created_at":"2026-07-05T08:07:29.602235+00:00"},{"alias_kind":"pith_short_16","alias_value":"QH7HFXFE6UMXENAL","created_at":"2026-07-05T08:07:29.602235+00:00"},{"alias_kind":"pith_short_8","alias_value":"QH7HFXFE","created_at":"2026-07-05T08:07:29.602235+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28044","citing_title":"A Tree-of-Thoughts Inspired Hybrid Approach for Legal Case Judgement Summarization using LLMs","ref_index":249,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28073","citing_title":"StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16377","citing_title":"CheckSupport: A Local LLM-Powered Tool for Automated Manuscript Submission Checklist Selection and Completion","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2601.21459","citing_title":"HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07068","citing_title":"WiCER: Wiki-memory Compile, Evaluate, Refine Iterative Knowledge Compilation for LLM Wiki Systems","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17648","citing_title":"ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20131","citing_title":"Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives","ref_index":152,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6","json":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6.json","graph_json":"https://pith.science/api/pith-number/QH7HFXFE6UMXENALHXSMU4NJU6/graph.json","events_json":"https://pith.science/api/pith-number/QH7HFXFE6UMXENALHXSMU4NJU6/events.json","paper":"https://pith.science/paper/QH7HFXFE"},"agent_actions":{"view_html":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6","download_json":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6.json","view_paper":"https://pith.science/paper/QH7HFXFE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.00785&json=true","fetch_graph":"https://pith.science/api/pith-number/QH7HFXFE6UMXENALHXSMU4NJU6/graph.json","fetch_events":"https://pith.science/api/pith-number/QH7HFXFE6UMXENALHXSMU4NJU6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6/action/storage_attestation","attest_author":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6/action/author_attestation","sign_citation":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6/action/citation_signature","submit_replication":"https://pith.science/pith/QH7HFXFE6UMXENALHXSMU4NJU6/action/replication_record"}},"created_at":"2026-07-05T08:07:29.602235+00:00","updated_at":"2026-07-05T08:07:29.602235+00:00"}