{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:TL3SYITLLL5CCR3JF3K7R5244D","short_pith_number":"pith:TL3SYITL","schema_version":"1.0","canonical_sha256":"9af72c226b5afa2147692ed5f8f75ce0de98a4bb26eb229ffc784f68702b6ca1","source":{"kind":"arxiv","id":"1606.02785","version":1},"attestation_state":"computed","paper":{"title":"Neural Network-Based Abstract Generation for Opinions and Arguments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Lu Wang, Wang Ling","submitted_at":"2016-06-09T00:15:23Z","abstract_excerpt":"We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the encoder to integrate information from an important subset of input. Automatic evaluation indicates that our system outperforms state-of-the-art abstractive and extractive summarization systems on two newly collected datasets of movie reviews and arguments. Our system summaries are also rated"},"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":"1606.02785","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-06-09T00:15:23Z","cross_cats_sorted":[],"title_canon_sha256":"1ebfb6caa295ab1196135510a39fce68ceceb780f2774aee4edf77c6cf7804f5","abstract_canon_sha256":"d586a4a79ef346fa547cf8dfaab2a8341d3ec90289db1b3bd848be1a0ee21058"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:40.023155Z","signature_b64":"YiypYoMw8mENUwswXzHD3BXoICxso2v60G67+ZBqwg4Xmyqpz74ioS/jhLFO9b9B85/p84y3XFhKs+2y+OMwCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9af72c226b5afa2147692ed5f8f75ce0de98a4bb26eb229ffc784f68702b6ca1","last_reissued_at":"2026-05-18T01:12:40.022826Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:40.022826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Network-Based Abstract Generation for Opinions and Arguments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Lu Wang, Wang Ling","submitted_at":"2016-06-09T00:15:23Z","abstract_excerpt":"We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the encoder to integrate information from an important subset of input. Automatic evaluation indicates that our system outperforms state-of-the-art abstractive and extractive summarization systems on two newly collected datasets of movie reviews and arguments. Our system summaries are also rated"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.02785","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":""},"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":"1606.02785","created_at":"2026-05-18T01:12:40.022882+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.02785v1","created_at":"2026-05-18T01:12:40.022882+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.02785","created_at":"2026-05-18T01:12:40.022882+00:00"},{"alias_kind":"pith_short_12","alias_value":"TL3SYITLLL5C","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"TL3SYITLLL5CCR3J","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"TL3SYITL","created_at":"2026-05-18T12:30:44.179134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D","json":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D.json","graph_json":"https://pith.science/api/pith-number/TL3SYITLLL5CCR3JF3K7R5244D/graph.json","events_json":"https://pith.science/api/pith-number/TL3SYITLLL5CCR3JF3K7R5244D/events.json","paper":"https://pith.science/paper/TL3SYITL"},"agent_actions":{"view_html":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D","download_json":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D.json","view_paper":"https://pith.science/paper/TL3SYITL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.02785&json=true","fetch_graph":"https://pith.science/api/pith-number/TL3SYITLLL5CCR3JF3K7R5244D/graph.json","fetch_events":"https://pith.science/api/pith-number/TL3SYITLLL5CCR3JF3K7R5244D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D/action/storage_attestation","attest_author":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D/action/author_attestation","sign_citation":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D/action/citation_signature","submit_replication":"https://pith.science/pith/TL3SYITLLL5CCR3JF3K7R5244D/action/replication_record"}},"created_at":"2026-05-18T01:12:40.022882+00:00","updated_at":"2026-05-18T01:12:40.022882+00:00"}