{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:D44IIGQMNWQAWJODD7PBW7IB7Q","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":"7e2ca15d40ec147ce34c68a9af0b21f3d3e78cd92e3844b795cf9a6697c0d95d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-23T04:22:33Z","title_canon_sha256":"82acaa51ef1dad5572e16cee9ae908b944d1ea04480771cbe97ed8eccc7fe688"},"schema_version":"1.0","source":{"id":"2108.01064","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.01064","created_at":"2026-07-05T03:02:35Z"},{"alias_kind":"arxiv_version","alias_value":"2108.01064v1","created_at":"2026-07-05T03:02:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.01064","created_at":"2026-07-05T03:02:35Z"},{"alias_kind":"pith_short_12","alias_value":"D44IIGQMNWQA","created_at":"2026-07-05T03:02:35Z"},{"alias_kind":"pith_short_16","alias_value":"D44IIGQMNWQAWJOD","created_at":"2026-07-05T03:02:35Z"},{"alias_kind":"pith_short_8","alias_value":"D44IIGQM","created_at":"2026-07-05T03:02:35Z"}],"graph_snapshots":[{"event_id":"sha256:f0e221f64f814873d3ebd6d8fcad16eb018bfa7bdece130585af49852bcb5d5e","target":"graph","created_at":"2026-07-05T03:02:35Z","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/2108.01064/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The amount of text data available online is increasing at a very fast pace hence text summarization has become essential. Most of the modern recommender and text classification systems require going through a huge amount of data. Manually generating precise and fluent summaries of lengthy articles is a very tiresome and time-consuming task. Hence generating automated summaries for the data and using it to train machine learning models will make these models space and time-efficient. Extractive summarization and abstractive summarization are two separate methods of generating summaries. The ext","authors_text":"Anjum, Anushka Gupta, Diksha Chugh, Rahul Katarya","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-23T04:22:33Z","title":"Automated News Summarization Using Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.01064","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":"sha256:4a3e54f3a84f585d51df47ffa1eae415c6a6940fa26eaeb1092445f8f80d4c24","target":"record","created_at":"2026-07-05T03:02:35Z","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":"7e2ca15d40ec147ce34c68a9af0b21f3d3e78cd92e3844b795cf9a6697c0d95d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-04-23T04:22:33Z","title_canon_sha256":"82acaa51ef1dad5572e16cee9ae908b944d1ea04480771cbe97ed8eccc7fe688"},"schema_version":"1.0","source":{"id":"2108.01064","kind":"arxiv","version":1}},"canonical_sha256":"1f38841a0c6da00b25c31fde1b7d01fc12cd92e71062a813000e26581bbcb5a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f38841a0c6da00b25c31fde1b7d01fc12cd92e71062a813000e26581bbcb5a3","first_computed_at":"2026-07-05T03:02:35.324699Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:02:35.324699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xu8SVKH7ptMNH+hvRMHqV1VNTmMEtotLAyeTMKNifSagEyoOshzpbLsnVDeONDSf6P7GX8vssG/vYrMjeHlEBw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:02:35.325101Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.01064","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4a3e54f3a84f585d51df47ffa1eae415c6a6940fa26eaeb1092445f8f80d4c24","sha256:f0e221f64f814873d3ebd6d8fcad16eb018bfa7bdece130585af49852bcb5d5e"],"state_sha256":"9e80f0c27babd6266488ad8fe607b9167fb251c2b6eac8bca25b17df74b0b130"}