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Paper Citation Record · LEDGER

Coverage-based Fairness in Multi-document Summarization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2412.08795.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.08795 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:41:12.809512Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T05:41:13.107173Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c93b6c80-52a2-4942-b865-8e5052d44c33 · inbound

Improving Fairness of Large Language Models in Multi-document Summarization cites this paper.

Improving Fairness of Large Language Models in Multi-document Summarization Coverage-based Fairness in Multi-document Summarization

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:41:13.115968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T05:41:12.809512Z digest=sha256:ae815d64fd89aa9b6be72acf9b392c8e75656f208dfd90d4805e4531097c3dee

Observation b1d23379-907c-4ad6-9fd6-4d7e837d34a8 · inbound

Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization cites this paper.

Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization Coverage-based Fairness in Multi-document Summarization

Reference 17

Resolution
unresolved
no resolver link, observed 2026-07-14T08:58:37.267426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T08:58:37.267426Z digest=sha256:8fc5156264f1ef0ed5f961c90629966fa10b7021dfc84161317dc78c50c74410