{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:YHTCYQB4MP47CDFP5LXACU7DZE","short_pith_number":"pith:YHTCYQB4","canonical_record":{"source":{"id":"2109.08565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T14:23:58Z","cross_cats_sorted":[],"title_canon_sha256":"ddb38370667d04f44ff7f9f8ff5911f785a7d9b29dd4be378df7e1e0f2eab781","abstract_canon_sha256":"8744d18ea0ef9b8350704044b2212a9435a3d2bd7517acc1a8a7dcddf696cfc7"},"schema_version":"1.0"},"canonical_sha256":"c1e62c403c63f9f10cafeaee0153e3c934018c486ac7582a84b120b21c006781","source":{"kind":"arxiv","id":"2109.08565","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08565","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08565v1","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08565","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"pith_short_12","alias_value":"YHTCYQB4MP47","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"pith_short_16","alias_value":"YHTCYQB4MP47CDFP","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"pith_short_8","alias_value":"YHTCYQB4","created_at":"2026-07-05T03:15:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:YHTCYQB4MP47CDFP5LXACU7DZE","target":"record","payload":{"canonical_record":{"source":{"id":"2109.08565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T14:23:58Z","cross_cats_sorted":[],"title_canon_sha256":"ddb38370667d04f44ff7f9f8ff5911f785a7d9b29dd4be378df7e1e0f2eab781","abstract_canon_sha256":"8744d18ea0ef9b8350704044b2212a9435a3d2bd7517acc1a8a7dcddf696cfc7"},"schema_version":"1.0"},"canonical_sha256":"c1e62c403c63f9f10cafeaee0153e3c934018c486ac7582a84b120b21c006781","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:15:17.381223Z","signature_b64":"FN0Ir68VLoMvW1Jg7uNZBR5KOQnEPnSoba9HjGMb2OkFa7v6DR8nJugLqB8xJOiSamYEV9d1m/23UW2fpQ4MAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1e62c403c63f9f10cafeaee0153e3c934018c486ac7582a84b120b21c006781","last_reissued_at":"2026-07-05T03:15:17.380817Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:15:17.380817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.08565","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":"2026-07-05T03:15:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ntElo2ttyWs7oI/HJyCF7dRChHAaVWCmqWvvVcxi5TbzJShZD0fnopfVmZ+Nwg0pTYmZMY0eugSXB8/sc3biDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T20:57:48.353299Z"},"content_sha256":"4643f95c3a8619e3306c13e51b6cdcd508c0ff8fc1f67691f3cd9c8228f1bbcd","schema_version":"1.0","event_id":"sha256:4643f95c3a8619e3306c13e51b6cdcd508c0ff8fc1f67691f3cd9c8228f1bbcd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:YHTCYQB4MP47CDFP5LXACU7DZE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploring Multitask Learning for Low-Resource AbstractiveSummarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ahmed Magooda, Diane Litman, Mohamed Elaraby","submitted_at":"2021-09-17T14:23:58Z","abstract_excerpt":"This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning. We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08565","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/2109.08565/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":"2026-07-05T03:15:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aaiv9WMsSNuDq+kG5zEh2P6tpaH1KyVMS0lCLY4EZBsKuOd952PKVIaVLIg1/n3oYJWyy35u6XGozm9JAI3PAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T20:57:48.353683Z"},"content_sha256":"fa5cff13bd12f99bb931aa1913df92b98f00bd5afd15da8dfa6dcf2ded2b2cd0","schema_version":"1.0","event_id":"sha256:fa5cff13bd12f99bb931aa1913df92b98f00bd5afd15da8dfa6dcf2ded2b2cd0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YHTCYQB4MP47CDFP5LXACU7DZE/bundle.json","state_url":"https://pith.science/pith/YHTCYQB4MP47CDFP5LXACU7DZE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YHTCYQB4MP47CDFP5LXACU7DZE/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":"2026-07-10T20:57:48Z","links":{"resolver":"https://pith.science/pith/YHTCYQB4MP47CDFP5LXACU7DZE","bundle":"https://pith.science/pith/YHTCYQB4MP47CDFP5LXACU7DZE/bundle.json","state":"https://pith.science/pith/YHTCYQB4MP47CDFP5LXACU7DZE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YHTCYQB4MP47CDFP5LXACU7DZE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:YHTCYQB4MP47CDFP5LXACU7DZE","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":"8744d18ea0ef9b8350704044b2212a9435a3d2bd7517acc1a8a7dcddf696cfc7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T14:23:58Z","title_canon_sha256":"ddb38370667d04f44ff7f9f8ff5911f785a7d9b29dd4be378df7e1e0f2eab781"},"schema_version":"1.0","source":{"id":"2109.08565","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08565","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08565v1","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08565","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"pith_short_12","alias_value":"YHTCYQB4MP47","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"pith_short_16","alias_value":"YHTCYQB4MP47CDFP","created_at":"2026-07-05T03:15:17Z"},{"alias_kind":"pith_short_8","alias_value":"YHTCYQB4","created_at":"2026-07-05T03:15:17Z"}],"graph_snapshots":[{"event_id":"sha256:fa5cff13bd12f99bb931aa1913df92b98f00bd5afd15da8dfa6dcf2ded2b2cd0","target":"graph","created_at":"2026-07-05T03:15:17Z","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/2109.08565/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning. We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced.","authors_text":"Ahmed Magooda, Diane Litman, Mohamed Elaraby","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T14:23:58Z","title":"Exploring Multitask Learning for Low-Resource AbstractiveSummarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08565","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":"sha256:4643f95c3a8619e3306c13e51b6cdcd508c0ff8fc1f67691f3cd9c8228f1bbcd","target":"record","created_at":"2026-07-05T03:15:17Z","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":"8744d18ea0ef9b8350704044b2212a9435a3d2bd7517acc1a8a7dcddf696cfc7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-17T14:23:58Z","title_canon_sha256":"ddb38370667d04f44ff7f9f8ff5911f785a7d9b29dd4be378df7e1e0f2eab781"},"schema_version":"1.0","source":{"id":"2109.08565","kind":"arxiv","version":1}},"canonical_sha256":"c1e62c403c63f9f10cafeaee0153e3c934018c486ac7582a84b120b21c006781","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c1e62c403c63f9f10cafeaee0153e3c934018c486ac7582a84b120b21c006781","first_computed_at":"2026-07-05T03:15:17.380817Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:15:17.380817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FN0Ir68VLoMvW1Jg7uNZBR5KOQnEPnSoba9HjGMb2OkFa7v6DR8nJugLqB8xJOiSamYEV9d1m/23UW2fpQ4MAg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:15:17.381223Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.08565","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4643f95c3a8619e3306c13e51b6cdcd508c0ff8fc1f67691f3cd9c8228f1bbcd","sha256:fa5cff13bd12f99bb931aa1913df92b98f00bd5afd15da8dfa6dcf2ded2b2cd0"],"state_sha256":"0e7cf5613d2b2d2673e857f7222b5191c23ff5c925ba9e5acdda35d2c53e8d0f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XgALw3LiFdqqJ7ZeQpoV352Uy1Z/Q9Nv9aOXifFtaYTnwfNMo2xl5Q1HAkc7sTMmA1I6eIlHl2pQJEo0oaC0CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T20:57:48.356032Z","bundle_sha256":"17d1be7165bd316ebf9cf592d5f555e91861125ef7ef87e18e5d06a8def0a281"}}