{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:L4OJ2FTQ33XFSQR6XFLY52RHPZ","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":"ed7092a4e459689824ba1975be59d5237b29dc82c769dc9974606b0e0a845309","cross_cats_sorted":["cs.CL","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-15T20:26:44Z","title_canon_sha256":"cc1e171481000e604af85d2c72c0f03c9bb32718fd53999d813ad3091d002111"},"schema_version":"1.0","source":{"id":"1810.06667","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.06667","created_at":"2026-05-17T23:55:34Z"},{"alias_kind":"arxiv_version","alias_value":"1810.06667v2","created_at":"2026-05-17T23:55:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.06667","created_at":"2026-05-17T23:55:34Z"},{"alias_kind":"pith_short_12","alias_value":"L4OJ2FTQ33XF","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"L4OJ2FTQ33XFSQR6","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"L4OJ2FTQ","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:2a659e2a91228bf4e603ff487886788533c7f3f0e97fc29590103fb85b4d9f73","target":"graph","created_at":"2026-05-17T23:55:34Z","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"},"paper":{"abstract_excerpt":"Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explored as in areas such as image analysis. In this paper, we study the problem of transfer learning for text summarization and discuss why existing state-of-the-art models fail to generalize well on other (unseen) datasets. We propose a reinforcement learning framework based on a self-critic policy gradient approach which achieves good generalization and state-of-the-art results o","authors_text":"Chandan K. Reddy, Naren Ramakrishnan, Yaser Keneshloo","cross_cats":["cs.CL","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-15T20:26:44Z","title":"Deep Transfer Reinforcement Learning for Text Summarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.06667","kind":"arxiv","version":2},"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":"sha256:3cb2e531111a1f5e338289e2fc3c6d34512ad27a08120429cfbf51c908d5c541","target":"record","created_at":"2026-05-17T23:55:34Z","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":"ed7092a4e459689824ba1975be59d5237b29dc82c769dc9974606b0e0a845309","cross_cats_sorted":["cs.CL","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-15T20:26:44Z","title_canon_sha256":"cc1e171481000e604af85d2c72c0f03c9bb32718fd53999d813ad3091d002111"},"schema_version":"1.0","source":{"id":"1810.06667","kind":"arxiv","version":2}},"canonical_sha256":"5f1c9d1670deee59423eb9578eea277e4bc37c5e32f7e21ff417e605d6eb0845","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5f1c9d1670deee59423eb9578eea277e4bc37c5e32f7e21ff417e605d6eb0845","first_computed_at":"2026-05-17T23:55:34.013281Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:34.013281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RH0LQq9eVnAzgjTF/xT2UVLXK/8NlXUO+pb5h650G8WgUAkBO1oJU4SWSwH8zkIFLjVcBPJ8CYyi1Hq5LUWwBw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:34.013838Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.06667","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3cb2e531111a1f5e338289e2fc3c6d34512ad27a08120429cfbf51c908d5c541","sha256:2a659e2a91228bf4e603ff487886788533c7f3f0e97fc29590103fb85b4d9f73"],"state_sha256":"45ef14ccff92cc2a58d9f8076a14f10aac19ea93dfea70034eebd2aae6958fab"}