Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1912.08777.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:06:05.111204Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
981
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 4f8a22c1-c597-493a-b0a0-4e493ddaa8f6 · inbound
Learning to summarize from human feedback cites this paper.
Learning to summarize from human feedback PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 70
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T01:46:18.702624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 452955ac-d86d-4042-90e7-10ae54aa0448 · inbound
Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions cites this paper.
Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:06:05.111204Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 986c611a-1010-4a15-8533-14f5da05324d · inbound
LLMs as Architects and Critics for Multi-Source Opinion Summarization cites this paper.
LLMs as Architects and Critics for Multi-Source Opinion Summarization PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:46:51.092533Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6c940163-6683-44ac-987e-af0d78eeb4cc · inbound
Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models cites this paper.
Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T19:33:20.072296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 51c73a60-3044-4a68-857c-aa6338ddaa93 · inbound
Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models cites this paper.
Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T16:44:16.083763Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3ead7449-6e30-40a1-9502-d6a95d75cfb5 · inbound
From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction cites this paper.
From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T10:16:27.496929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 42265479-151d-4287-8f69-9c9caa47beb9 · inbound
SWAN: Semantic Watermarking with Abstract Meaning Representation cites this paper.
SWAN: Semantic Watermarking with Abstract Meaning Representation PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:51:09.597617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9ccb029d-f194-46a6-a9e4-fd25e9fa057b · inbound
Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding cites this paper.
Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T07:43:13.121177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3f1667f8-05ae-41d9-8408-4f745d97be54 · inbound
Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector cites this paper.
Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:27.171969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.