{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:TBR4B4TL5A4QYWIJXFPQP4Y5GB","short_pith_number":"pith:TBR4B4TL","canonical_record":{"source":{"id":"1908.09119","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-24T10:05:40Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"8dcd9379b686224cfb4c34fba683a058b90bca72eab59a4118dee4afc4ab9dac","abstract_canon_sha256":"ec7e952b1e109c47383daeeb7cca9e9418770ebc1e88850f43412a3f2c9e5a50"},"schema_version":"1.0"},"canonical_sha256":"9863c0f26be8390c5909b95f07f31d305cecc6146c3577a87e4a71fd40052cf4","source":{"kind":"arxiv","id":"1908.09119","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.09119","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"arxiv_version","alias_value":"1908.09119v1","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09119","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"pith_short_12","alias_value":"TBR4B4TL5A4Q","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"pith_short_16","alias_value":"TBR4B4TL5A4QYWIJ","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"pith_short_8","alias_value":"TBR4B4TL","created_at":"2026-07-04T23:59:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:TBR4B4TL5A4QYWIJXFPQP4Y5GB","target":"record","payload":{"canonical_record":{"source":{"id":"1908.09119","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-24T10:05:40Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"8dcd9379b686224cfb4c34fba683a058b90bca72eab59a4118dee4afc4ab9dac","abstract_canon_sha256":"ec7e952b1e109c47383daeeb7cca9e9418770ebc1e88850f43412a3f2c9e5a50"},"schema_version":"1.0"},"canonical_sha256":"9863c0f26be8390c5909b95f07f31d305cecc6146c3577a87e4a71fd40052cf4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:36.909698Z","signature_b64":"x08g8IA9xTYiEGK9w+mvBxoDULwNRXgP03sRbsD79Y9cebK0cUDjvpAyjNps5AhhI8aGJrGBLlz8Nc7APdMRBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9863c0f26be8390c5909b95f07f31d305cecc6146c3577a87e4a71fd40052cf4","last_reissued_at":"2026-07-04T23:59:36.909194Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:36.909194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.09119","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:59:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7SpdT+tBN883SDcL5NOaM5UMJmSOiZLLBBAlNKF0Qv8RhSOUCRDpTkkAF6VXDhC6NaziwR9aQ+ZgEFaK1J/fCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T06:32:20.885316Z"},"content_sha256":"147b4d4418f39ab3c4b80d7705e4a4dea39c93581749bb3643d08615ac1dc2ea","schema_version":"1.0","event_id":"sha256:147b4d4418f39ab3c4b80d7705e4a4dea39c93581749bb3643d08615ac1dc2ea"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:TBR4B4TL5A4QYWIJXFPQP4Y5GB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic Text Summarization of Legal Cases: A Hybrid Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Varun Pandya","submitted_at":"2019-08-24T10:05:40Z","abstract_excerpt":"Manual Summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain. Lawyers spend a lot of time preparing legal briefs of their clients' case files. Automatic Text summarization is a constantly evolving field of Natural Language Processing(NLP), which is a subdiscipline of the Artificial Intelligence Field. In this paper a hybrid method for automatic text summarization of legal cases using k-means clustering technique and tf-idf(term frequency-inverse document frequency) word vectorizer is proposed. The summary generated by the proposed method "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09119","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/1908.09119/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:59:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iU1yjK6Z4ImmH9VhguZhO2grQxUF1ymdBfKImgz/sPrk0VhOiqZIO/perywu6YOO+3hfjuAUvm8d8Bv4X1TKAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T06:32:20.885893Z"},"content_sha256":"5db0dbb08f42655ff60f309070d5ef93ee972d205ea7ccb22aedcfc2e8d4ce8e","schema_version":"1.0","event_id":"sha256:5db0dbb08f42655ff60f309070d5ef93ee972d205ea7ccb22aedcfc2e8d4ce8e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TBR4B4TL5A4QYWIJXFPQP4Y5GB/bundle.json","state_url":"https://pith.science/pith/TBR4B4TL5A4QYWIJXFPQP4Y5GB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TBR4B4TL5A4QYWIJXFPQP4Y5GB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-20T06:32:20Z","links":{"resolver":"https://pith.science/pith/TBR4B4TL5A4QYWIJXFPQP4Y5GB","bundle":"https://pith.science/pith/TBR4B4TL5A4QYWIJXFPQP4Y5GB/bundle.json","state":"https://pith.science/pith/TBR4B4TL5A4QYWIJXFPQP4Y5GB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TBR4B4TL5A4QYWIJXFPQP4Y5GB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:TBR4B4TL5A4QYWIJXFPQP4Y5GB","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":"ec7e952b1e109c47383daeeb7cca9e9418770ebc1e88850f43412a3f2c9e5a50","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-24T10:05:40Z","title_canon_sha256":"8dcd9379b686224cfb4c34fba683a058b90bca72eab59a4118dee4afc4ab9dac"},"schema_version":"1.0","source":{"id":"1908.09119","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.09119","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"arxiv_version","alias_value":"1908.09119v1","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09119","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"pith_short_12","alias_value":"TBR4B4TL5A4Q","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"pith_short_16","alias_value":"TBR4B4TL5A4QYWIJ","created_at":"2026-07-04T23:59:36Z"},{"alias_kind":"pith_short_8","alias_value":"TBR4B4TL","created_at":"2026-07-04T23:59:36Z"}],"graph_snapshots":[{"event_id":"sha256:5db0dbb08f42655ff60f309070d5ef93ee972d205ea7ccb22aedcfc2e8d4ce8e","target":"graph","created_at":"2026-07-04T23:59:36Z","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/1908.09119/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Manual Summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain. Lawyers spend a lot of time preparing legal briefs of their clients' case files. Automatic Text summarization is a constantly evolving field of Natural Language Processing(NLP), which is a subdiscipline of the Artificial Intelligence Field. In this paper a hybrid method for automatic text summarization of legal cases using k-means clustering technique and tf-idf(term frequency-inverse document frequency) word vectorizer is proposed. The summary generated by the proposed method ","authors_text":"Varun Pandya","cross_cats":["cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-24T10:05:40Z","title":"Automatic Text Summarization of Legal Cases: A Hybrid Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09119","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":"sha256:147b4d4418f39ab3c4b80d7705e4a4dea39c93581749bb3643d08615ac1dc2ea","target":"record","created_at":"2026-07-04T23:59:36Z","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":"ec7e952b1e109c47383daeeb7cca9e9418770ebc1e88850f43412a3f2c9e5a50","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-24T10:05:40Z","title_canon_sha256":"8dcd9379b686224cfb4c34fba683a058b90bca72eab59a4118dee4afc4ab9dac"},"schema_version":"1.0","source":{"id":"1908.09119","kind":"arxiv","version":1}},"canonical_sha256":"9863c0f26be8390c5909b95f07f31d305cecc6146c3577a87e4a71fd40052cf4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9863c0f26be8390c5909b95f07f31d305cecc6146c3577a87e4a71fd40052cf4","first_computed_at":"2026-07-04T23:59:36.909194Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:59:36.909194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x08g8IA9xTYiEGK9w+mvBxoDULwNRXgP03sRbsD79Y9cebK0cUDjvpAyjNps5AhhI8aGJrGBLlz8Nc7APdMRBw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:59:36.909698Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.09119","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:147b4d4418f39ab3c4b80d7705e4a4dea39c93581749bb3643d08615ac1dc2ea","sha256:5db0dbb08f42655ff60f309070d5ef93ee972d205ea7ccb22aedcfc2e8d4ce8e"],"state_sha256":"8427eaa3681206d487eb6b97ec0a2a3752dda131ae3b28cec875063c373e14c1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pv+jdsOKSXrDz9AiZml8KsQdF4EqXOBkX2IgZmOLZAc8pqJ2ighe/V/oL3/FXIiXgbBPAPmLV4vHv8onyOxCCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T06:32:20.893011Z","bundle_sha256":"1328379f536383fe098618f60ca23613011cd3c29cbd11296e9c1008ceecae01"}}