{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:I3UT5TZBS3YZHWS4OW5BCLC6OA","short_pith_number":"pith:I3UT5TZB","schema_version":"1.0","canonical_sha256":"46e93ecf2196f193da5c75ba112c5e70210d593cf70b784ff9b32917997b9418","source":{"kind":"arxiv","id":"2011.04843","version":3},"attestation_state":"computed","paper":{"title":"Multi-document Summarization via Deep Learning Techniques: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Congbo Ma, Hu Wang, Mingyu Guo, Quan Z. Sheng, Wei Emma Zhang","submitted_at":"2020-11-10T00:35:46Z","abstract_excerpt":"Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our survey, the first of its kind, systematically overviews the recent deep learning based MDS models. We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences between various objective functions that are rarely discussed in the existing literature. Finally, we propose several future directions pertaining t"},"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":"2011.04843","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-11-10T00:35:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1c2140bd5742761057c27b4512ceee41e3db9d9d8a7991836d5413be426a9cbc","abstract_canon_sha256":"b68e9e3ef6acdd7f7296266a119e95611d20343da289152cccebae1bdd1a744c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:39:12.244016Z","signature_b64":"NHqKc0jdQd8+oRY3R26He0lpidITmU2H7mOkIqAIoS8zG/vcEWbbXCJcdNapWjr3SoB9OIotlfpX0M/4P7H/Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46e93ecf2196f193da5c75ba112c5e70210d593cf70b784ff9b32917997b9418","last_reissued_at":"2026-07-05T03:39:12.243638Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:39:12.243638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-document Summarization via Deep Learning Techniques: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Congbo Ma, Hu Wang, Mingyu Guo, Quan Z. Sheng, Wei Emma Zhang","submitted_at":"2020-11-10T00:35:46Z","abstract_excerpt":"Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our survey, the first of its kind, systematically overviews the recent deep learning based MDS models. We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences between various objective functions that are rarely discussed in the existing literature. Finally, we propose several future directions pertaining t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.04843","kind":"arxiv","version":3},"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/2011.04843/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2011.04843","created_at":"2026-07-05T03:39:12.243685+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.04843v3","created_at":"2026-07-05T03:39:12.243685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.04843","created_at":"2026-07-05T03:39:12.243685+00:00"},{"alias_kind":"pith_short_12","alias_value":"I3UT5TZBS3YZ","created_at":"2026-07-05T03:39:12.243685+00:00"},{"alias_kind":"pith_short_16","alias_value":"I3UT5TZBS3YZHWS4","created_at":"2026-07-05T03:39:12.243685+00:00"},{"alias_kind":"pith_short_8","alias_value":"I3UT5TZB","created_at":"2026-07-05T03:39:12.243685+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/I3UT5TZBS3YZHWS4OW5BCLC6OA","json":"https://pith.science/pith/I3UT5TZBS3YZHWS4OW5BCLC6OA.json","graph_json":"https://pith.science/api/pith-number/I3UT5TZBS3YZHWS4OW5BCLC6OA/graph.json","events_json":"https://pith.science/api/pith-number/I3UT5TZBS3YZHWS4OW5BCLC6OA/events.json","paper":"https://pith.science/paper/I3UT5TZB"},"agent_actions":{"view_html":"https://pith.science/pith/I3UT5TZBS3YZHWS4OW5BCLC6OA","download_json":"https://pith.science/pith/I3UT5TZBS3YZHWS4OW5BCLC6OA.json","view_paper":"https://pith.science/paper/I3UT5TZB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.04843&json=true","fetch_graph":"https://pith.science/api/pith-number/I3UT5TZBS3YZHWS4OW5BCLC6OA/graph.json","fetch_events":"https://pith.science/api/pith-number/I3UT5TZBS3YZHWS4OW5BCLC6OA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I3UT5TZBS3YZHWS4OW5BCLC6OA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I3UT5TZBS3YZHWS4OW5BCLC6OA/action/storage_attestation","attest_author":"https://pith.science/pith/I3UT5TZBS3YZHWS4OW5BCLC6OA/action/author_attestation","sign_citation":"https://pith.science/pith/I3UT5TZBS3YZHWS4OW5BCLC6OA/action/citation_signature","submit_replication":"https://pith.science/pith/I3UT5TZBS3YZHWS4OW5BCLC6OA/action/replication_record"}},"created_at":"2026-07-05T03:39:12.243685+00:00","updated_at":"2026-07-05T03:39:12.243685+00:00"}