Paper Citation Record · LEDGER

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics

As of 9 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2605.26840.

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

pith.paper-citation-record.v1
2605.26840 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T18:32:27.664424Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

11 of 11 outbound references displayed

verified exact3
verified fuzzy0
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3476f7f2-e849-4b4a-be22-931937ffce2a · outbound

This paper cites Factuality challenges in the era of large language models.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics Factuality challenges in the era of large language models

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T18:33:50.306034Z

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 b021938b-cce8-4c24-a093-9bf1362b39e0 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics The Curious Case of Neural Text Degeneration

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T18:33:50.300698Z

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 8819f082-2fd3-420d-acc8-4513873b82fe · outbound

This paper cites ChatGPT as a Factual Inconsistency Evaluator for Text Summarization.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics ChatGPT as a Factual Inconsistency Evaluator for Text Summarization

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:33:50.303497Z

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 808b91e2-8e99-4395-8a8e-992031811804 · outbound

This paper cites InProceedings of the 2018 Conference on Empirical Methods in Natural Lan- guage Processing, pages 1797–1807, Brussels, Bel- gium.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics InProceedings of the 2018 Conference on Empirical Methods in Natural Lan- guage Processing, pages 1797–1807, Brussels, Bel- gium

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T18:32:27.664424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b742479e-c8ed-4e46-84f2-d85173cb2ca3 · outbound

This paper cites Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T18:32:27.664424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 512a0f95-9a4e-4e87-8a50-e60abe17ac53 · outbound

This paper cites QuestEval: Summarization Asks for Fact-based Evaluation.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics QuestEval: Summarization Asks for Fact-based Evaluation

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:33:50.298142Z

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 8bc8f20a-0589-4904-b5a0-c2a44dc70f9d · outbound

This paper cites InProceedings of the Thirty-Third Inter- national Joint Conference on Artificial Intelligence, IJCAI ’24.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics InProceedings of the Thirty-Third Inter- national Joint Conference on Artificial Intelligence, IJCAI ’24

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T18:32:27.664424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 602d09ae-c869-4da5-84c6-a29fcc872244 · outbound

This paper cites Yuxuan Ye, Edwin Simpson, and Raul Santos Rodriguez.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics Yuxuan Ye, Edwin Simpson, and Raul Santos Rodriguez

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T18:32:27.664424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7a6a673-9f91-4b0a-9940-875026262ffd · outbound

This paper cites Using Similarity to Evaluate Factual Consistency in Summaries.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics Using Similarity to Evaluate Factual Consistency in Summaries

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:33:50.295542Z

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 e9cacd69-1413-48f8-ab53-be7596feb768 · outbound

This paper cites InProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11328–11348, Toronto, Canada.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics InProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11328–11348, Toronto, Canada

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T18:32:27.664424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a1de725-f5a3-459e-8866-64bbd758c593 · outbound

This paper cites A\" or \.

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics A\" or \

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T18:32:27.664424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.