{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:U4XAZVBHB3F3NHU2ANJAAYBGJY","short_pith_number":"pith:U4XAZVBH","schema_version":"1.0","canonical_sha256":"a72e0cd4270ecbb69e9a03520060264e3969c17db4fa8b2cf7d443e96cef1f24","source":{"kind":"arxiv","id":"1804.05685","version":2},"attestation_state":"computed","paper":{"title":"A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arman Cohan, Doo Soon Kim, Franck Dernoncourt, Nazli Goharian, Seokhwan Kim, Trung Bui, Walter Chang","submitted_at":"2018-04-16T13:55:20Z","abstract_excerpt":"Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models."},"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":"1804.05685","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-16T13:55:20Z","cross_cats_sorted":[],"title_canon_sha256":"034a5592a1b5bb0769682f273b38200fb3f94c95edd5070b1c0f0ca64e6d2b3d","abstract_canon_sha256":"9c524881aa21e2e7b86559871116d99fc582417edb01cd3803fd810c44fe0133"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:27.733020Z","signature_b64":"xWDxzwSwkUil55m2kXgnZPD9X2H0BslOzsp1qCQgL85R0j5I5uQhWKGrln0Z0vbUnTrV4h5o0AJzsP7asCfyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a72e0cd4270ecbb69e9a03520060264e3969c17db4fa8b2cf7d443e96cef1f24","last_reissued_at":"2026-05-18T00:15:27.732236Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:27.732236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arman Cohan, Doo Soon Kim, Franck Dernoncourt, Nazli Goharian, Seokhwan Kim, Trung Bui, Walter Chang","submitted_at":"2018-04-16T13:55:20Z","abstract_excerpt":"Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.05685","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1804.05685","created_at":"2026-05-18T00:15:27.732370+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.05685v2","created_at":"2026-05-18T00:15:27.732370+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.05685","created_at":"2026-05-18T00:15:27.732370+00:00"},{"alias_kind":"pith_short_12","alias_value":"U4XAZVBHB3F3","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"U4XAZVBHB3F3NHU2","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"U4XAZVBH","created_at":"2026-05-18T12:32:56.356000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2109.10616","citing_title":"Enriching and Controlling Global Semantics for Text Summarization","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2605.22765","citing_title":"Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2605.15676","citing_title":"Dynamic Chunking for Diffusion Language Models","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2510.08055","citing_title":"From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.04595","citing_title":"A Queueing-Theoretic Framework for Stability Analysis of LLM Inference with KV Cache Memory Constraints","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY","json":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY.json","graph_json":"https://pith.science/api/pith-number/U4XAZVBHB3F3NHU2ANJAAYBGJY/graph.json","events_json":"https://pith.science/api/pith-number/U4XAZVBHB3F3NHU2ANJAAYBGJY/events.json","paper":"https://pith.science/paper/U4XAZVBH"},"agent_actions":{"view_html":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY","download_json":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY.json","view_paper":"https://pith.science/paper/U4XAZVBH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.05685&json=true","fetch_graph":"https://pith.science/api/pith-number/U4XAZVBHB3F3NHU2ANJAAYBGJY/graph.json","fetch_events":"https://pith.science/api/pith-number/U4XAZVBHB3F3NHU2ANJAAYBGJY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY/action/storage_attestation","attest_author":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY/action/author_attestation","sign_citation":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY/action/citation_signature","submit_replication":"https://pith.science/pith/U4XAZVBHB3F3NHU2ANJAAYBGJY/action/replication_record"}},"created_at":"2026-05-18T00:15:27.732370+00:00","updated_at":"2026-05-18T00:15:27.732370+00:00"}