{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZIZFQMAJKSA4IO6OV36URTWZWH","short_pith_number":"pith:ZIZFQMAJ","schema_version":"1.0","canonical_sha256":"ca325830095481c43bceaefd48ced9b1fa458eecb44d8659c91e0f4384e0db57","source":{"kind":"arxiv","id":"1909.02322","version":2},"attestation_state":"computed","paper":{"title":"Informative and Controllable Opinion Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mirella Lapata, Reinald Kim Amplayo","submitted_at":"2019-09-05T11:11:41Z","abstract_excerpt":"Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for each target can be prohibitively large, neural network-based methods follow a two-stage approach where an extractive step first pre-selects a subset of salient opinions and an abstractive step creates the summary while conditioning on the extracted subset. However, the extractive model leads to loss of information which may be useful depending on user needs. In this paper we propose a summarization framework that elim"},"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":"1909.02322","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-05T11:11:41Z","cross_cats_sorted":[],"title_canon_sha256":"fa0ea72e859bce681f0e5f80b7aac1201fe0ab47ce68f25a79e5d90ad79c62aa","abstract_canon_sha256":"7ec95b4bbbbcbf0b9c1105fa0914c34ef9fc7508de7e57d62853387cfbd0f93f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:08:47.549075Z","signature_b64":"/0J/Of1BmWJdVG0PV7iOWMv6Thr9NhcU9gbNVYCxc3hCHQw7y3EgYpum09JvC+bRzV2lWFcm0IGsslZta+KFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca325830095481c43bceaefd48ced9b1fa458eecb44d8659c91e0f4384e0db57","last_reissued_at":"2026-07-05T02:08:47.548596Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:08:47.548596Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Informative and Controllable Opinion Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mirella Lapata, Reinald Kim Amplayo","submitted_at":"2019-09-05T11:11:41Z","abstract_excerpt":"Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for each target can be prohibitively large, neural network-based methods follow a two-stage approach where an extractive step first pre-selects a subset of salient opinions and an abstractive step creates the summary while conditioning on the extracted subset. However, the extractive model leads to loss of information which may be useful depending on user needs. In this paper we propose a summarization framework that elim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.02322","kind":"arxiv","version":2},"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/1909.02322/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":"1909.02322","created_at":"2026-07-05T02:08:47.548653+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.02322v2","created_at":"2026-07-05T02:08:47.548653+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.02322","created_at":"2026-07-05T02:08:47.548653+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZIZFQMAJKSA4","created_at":"2026-07-05T02:08:47.548653+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZIZFQMAJKSA4IO6O","created_at":"2026-07-05T02:08:47.548653+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZIZFQMAJ","created_at":"2026-07-05T02:08:47.548653+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/ZIZFQMAJKSA4IO6OV36URTWZWH","json":"https://pith.science/pith/ZIZFQMAJKSA4IO6OV36URTWZWH.json","graph_json":"https://pith.science/api/pith-number/ZIZFQMAJKSA4IO6OV36URTWZWH/graph.json","events_json":"https://pith.science/api/pith-number/ZIZFQMAJKSA4IO6OV36URTWZWH/events.json","paper":"https://pith.science/paper/ZIZFQMAJ"},"agent_actions":{"view_html":"https://pith.science/pith/ZIZFQMAJKSA4IO6OV36URTWZWH","download_json":"https://pith.science/pith/ZIZFQMAJKSA4IO6OV36URTWZWH.json","view_paper":"https://pith.science/paper/ZIZFQMAJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.02322&json=true","fetch_graph":"https://pith.science/api/pith-number/ZIZFQMAJKSA4IO6OV36URTWZWH/graph.json","fetch_events":"https://pith.science/api/pith-number/ZIZFQMAJKSA4IO6OV36URTWZWH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZIZFQMAJKSA4IO6OV36URTWZWH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZIZFQMAJKSA4IO6OV36URTWZWH/action/storage_attestation","attest_author":"https://pith.science/pith/ZIZFQMAJKSA4IO6OV36URTWZWH/action/author_attestation","sign_citation":"https://pith.science/pith/ZIZFQMAJKSA4IO6OV36URTWZWH/action/citation_signature","submit_replication":"https://pith.science/pith/ZIZFQMAJKSA4IO6OV36URTWZWH/action/replication_record"}},"created_at":"2026-07-05T02:08:47.548653+00:00","updated_at":"2026-07-05T02:08:47.548653+00:00"}