{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:77Q42JPKT36JHRU7RA7U5LHIEZ","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":"87a1b44c3a00c1a1306ee456ffac6f07e3abda612c7e607b805f5d6e74c4987b","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2024-05-20T21:27:18Z","title_canon_sha256":"827c6e8587a44b0e0276b852ca9c4d643221a5e0a984680042129c3d6a3adf48"},"schema_version":"1.0","source":{"id":"2406.00013","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.00013","created_at":"2026-07-05T08:25:55Z"},{"alias_kind":"arxiv_version","alias_value":"2406.00013v1","created_at":"2026-07-05T08:25:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.00013","created_at":"2026-07-05T08:25:55Z"},{"alias_kind":"pith_short_12","alias_value":"77Q42JPKT36J","created_at":"2026-07-05T08:25:55Z"},{"alias_kind":"pith_short_16","alias_value":"77Q42JPKT36JHRU7","created_at":"2026-07-05T08:25:55Z"},{"alias_kind":"pith_short_8","alias_value":"77Q42JPK","created_at":"2026-07-05T08:25:55Z"}],"graph_snapshots":[{"event_id":"sha256:92d635f680d0a587843d739ac335f39d4c93395959cc6d88e2f06d6763fdb42d","target":"graph","created_at":"2026-07-05T08:25:55Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.00013/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatic summarization is the process of reducing a text document in order to generate a summary that retains the most important points of the original document. In this work, we study two problems - i) summarizing a text document as set of keywords/caption, for image recommedation, ii) generating opinion summary which good mix of relevancy and sentiment with the text document. Intially, we present our work on an recommending images for enhancing a substantial amount of existing plain text news articles. We use probabilistic models and word similarity heuristics to generate captions and extra","authors_text":"Jayaprakash Sundararaj","cross_cats":["cs.AI","cs.CL","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2024-05-20T21:27:18Z","title":"Thesis: Document Summarization with applications to Keyword extraction and Image Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.00013","kind":"arxiv","version":1},"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":"sha256:2e591a10981907c62795f76689e991a809c34097b92746c495171abbff0a3365","target":"record","created_at":"2026-07-05T08:25:55Z","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":"87a1b44c3a00c1a1306ee456ffac6f07e3abda612c7e607b805f5d6e74c4987b","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2024-05-20T21:27:18Z","title_canon_sha256":"827c6e8587a44b0e0276b852ca9c4d643221a5e0a984680042129c3d6a3adf48"},"schema_version":"1.0","source":{"id":"2406.00013","kind":"arxiv","version":1}},"canonical_sha256":"ffe1cd25ea9efc93c69f883f4eace8267c5df295046f7bfed683bfbc01a6a2d7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ffe1cd25ea9efc93c69f883f4eace8267c5df295046f7bfed683bfbc01a6a2d7","first_computed_at":"2026-07-05T08:25:55.426846Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:25:55.426846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k6fFnvx7JppRbnu7i48rgQauBElC1EtUEoY/5u7VaWeu5kxPYYhI9yQbK4wN4BGhlGBblxQxLMFeevJN7W6wDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:25:55.427360Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.00013","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2e591a10981907c62795f76689e991a809c34097b92746c495171abbff0a3365","sha256:92d635f680d0a587843d739ac335f39d4c93395959cc6d88e2f06d6763fdb42d"],"state_sha256":"f6caf30a8153a4d609b81eb88397c23e9694b9b13b6217926c4ce18884e22e04"}