{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:GMIKRVUC3FZG552TNRCPVSG56M","short_pith_number":"pith:GMIKRVUC","schema_version":"1.0","canonical_sha256":"3310a8d682d9726ef7536c44fac8ddf320c6173146dba8dfd79e1d576628c352","source":{"kind":"arxiv","id":"2010.08593","version":1},"attestation_state":"computed","paper":{"title":"Deep Submodular Networks for Extractive Data Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.IR"],"primary_cat":"cs.LG","authors_text":"Chandrashekhar Lavania, Jiten Girdhar, Rishabh Iyer, Suraj Kothawade","submitted_at":"2020-10-16T19:06:15Z","abstract_excerpt":"Deep Models are increasingly becoming prevalent in summarization problems (e.g. document, video and images) due to their ability to learn complex feature interactions and representations. However, they do not model characteristics such as diversity, representation, and coverage, which are also very important for summarization tasks. On the other hand, submodular functions naturally model these characteristics because of their diminishing returns property. Most approaches for modelling and learning submodular functions rely on very simple models, such as weighted mixtures of submodular function"},"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":"2010.08593","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-16T19:06:15Z","cross_cats_sorted":["cs.CL","cs.IR"],"title_canon_sha256":"241efd9235ad9594988c944b6a05f2892fa8c11c7d1d2ff6ae227ba4962734f5","abstract_canon_sha256":"6742718c880805d6deb2526c993a3deedd70ced5edf7cd5d3dc759da347d8861"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:44:00.144724Z","signature_b64":"B0F1pbYYuJNKcP6miTn4HpbQltzNZKYNJBnbT162oyhhLveW5geMC8prA9LZvIQkBef2+9DmrJWvh2RUwzNUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3310a8d682d9726ef7536c44fac8ddf320c6173146dba8dfd79e1d576628c352","last_reissued_at":"2026-07-05T01:44:00.144342Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:44:00.144342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Submodular Networks for Extractive Data Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.IR"],"primary_cat":"cs.LG","authors_text":"Chandrashekhar Lavania, Jiten Girdhar, Rishabh Iyer, Suraj Kothawade","submitted_at":"2020-10-16T19:06:15Z","abstract_excerpt":"Deep Models are increasingly becoming prevalent in summarization problems (e.g. document, video and images) due to their ability to learn complex feature interactions and representations. However, they do not model characteristics such as diversity, representation, and coverage, which are also very important for summarization tasks. On the other hand, submodular functions naturally model these characteristics because of their diminishing returns property. Most approaches for modelling and learning submodular functions rely on very simple models, such as weighted mixtures of submodular function"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.08593","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/2010.08593/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":"2010.08593","created_at":"2026-07-05T01:44:00.144406+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.08593v1","created_at":"2026-07-05T01:44:00.144406+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.08593","created_at":"2026-07-05T01:44:00.144406+00:00"},{"alias_kind":"pith_short_12","alias_value":"GMIKRVUC3FZG","created_at":"2026-07-05T01:44:00.144406+00:00"},{"alias_kind":"pith_short_16","alias_value":"GMIKRVUC3FZG552T","created_at":"2026-07-05T01:44:00.144406+00:00"},{"alias_kind":"pith_short_8","alias_value":"GMIKRVUC","created_at":"2026-07-05T01:44:00.144406+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.11239","citing_title":"Learning Set Functions with Implicit Differentiation","ref_index":46,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M","json":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M.json","graph_json":"https://pith.science/api/pith-number/GMIKRVUC3FZG552TNRCPVSG56M/graph.json","events_json":"https://pith.science/api/pith-number/GMIKRVUC3FZG552TNRCPVSG56M/events.json","paper":"https://pith.science/paper/GMIKRVUC"},"agent_actions":{"view_html":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M","download_json":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M.json","view_paper":"https://pith.science/paper/GMIKRVUC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.08593&json=true","fetch_graph":"https://pith.science/api/pith-number/GMIKRVUC3FZG552TNRCPVSG56M/graph.json","fetch_events":"https://pith.science/api/pith-number/GMIKRVUC3FZG552TNRCPVSG56M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M/action/storage_attestation","attest_author":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M/action/author_attestation","sign_citation":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M/action/citation_signature","submit_replication":"https://pith.science/pith/GMIKRVUC3FZG552TNRCPVSG56M/action/replication_record"}},"created_at":"2026-07-05T01:44:00.144406+00:00","updated_at":"2026-07-05T01:44:00.144406+00:00"}