{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:JT2CIHCO5SBIPYISUQ3DA73HTI","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":"470bf9c5b5440fbf9e8bcd6cb5b7781b2ce7270067f760954bc46bc86d68c087","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-11-09T14:12:09Z","title_canon_sha256":"b743cd2563264baadc8db9160b0c22a92ddd4930b670e188b73dfc78c94c764f"},"schema_version":"1.0","source":{"id":"2211.04903","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.04903","created_at":"2026-07-05T05:14:43Z"},{"alias_kind":"arxiv_version","alias_value":"2211.04903v1","created_at":"2026-07-05T05:14:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.04903","created_at":"2026-07-05T05:14:43Z"},{"alias_kind":"pith_short_12","alias_value":"JT2CIHCO5SBI","created_at":"2026-07-05T05:14:43Z"},{"alias_kind":"pith_short_16","alias_value":"JT2CIHCO5SBIPYIS","created_at":"2026-07-05T05:14:43Z"},{"alias_kind":"pith_short_8","alias_value":"JT2CIHCO","created_at":"2026-07-05T05:14:43Z"}],"graph_snapshots":[{"event_id":"sha256:d2c87b8d029247a250f70acd546ae6e74116e0d933554b82f703a485dbf68290","target":"graph","created_at":"2026-07-05T05:14:43Z","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/2211.04903/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter. We present a pipelined extractive-abstractive approach where the extractive step filters the content that is passed to the abstractive component. Extremely lengthy input also results in a highly skewed dataset towards negative instances for extractive summarization; we thus adopt a margin ranking loss for extraction to encourage separation between positive and negative examples. Our extraction component","authors_text":"Faisal Ladhak, Hardy Hardy, Kathleen McKeown, Miguel Ballesteros, Muhammad Khalifa, Vittorio Castelli","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-11-09T14:12:09Z","title":"Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.04903","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:a751535bf10212e5882a99acd7ce4a27f597c6d4590e9219b1dd64e1fadd8208","target":"record","created_at":"2026-07-05T05:14:43Z","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":"470bf9c5b5440fbf9e8bcd6cb5b7781b2ce7270067f760954bc46bc86d68c087","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-11-09T14:12:09Z","title_canon_sha256":"b743cd2563264baadc8db9160b0c22a92ddd4930b670e188b73dfc78c94c764f"},"schema_version":"1.0","source":{"id":"2211.04903","kind":"arxiv","version":1}},"canonical_sha256":"4cf4241c4eec8287e112a436307f679a07591deef7a15f8c89451d2f1c216c7c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4cf4241c4eec8287e112a436307f679a07591deef7a15f8c89451d2f1c216c7c","first_computed_at":"2026-07-05T05:14:43.372739Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:14:43.372739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Wmk6IxcUwOFfZGSMfPAAembxlbYYTmqWe5rMFK7FdlStT99KTm5jwQMrwXmRDeWEzpHlfkYWv6RkNaEIPDVDCw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:14:43.373172Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.04903","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a751535bf10212e5882a99acd7ce4a27f597c6d4590e9219b1dd64e1fadd8208","sha256:d2c87b8d029247a250f70acd546ae6e74116e0d933554b82f703a485dbf68290"],"state_sha256":"2f80db7b17c7c8c5a2269432d54b13adc996d78e1b301ba9e449df8fc4406452"}