Paper Citation Record · LEDGER

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models

As of 7 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2506.18036.

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

pith.paper-citation-record.v1
2506.18036 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:28:04.472497Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:29:56.532477Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:29:59.921236Z

Reference resolution

28 of 28 outbound references displayed

verified exact2
verified fuzzy9
unresolved15
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dbaa5fe5-a22d-4812-af6a-801e10f2c805 · outbound

This paper cites Expert Systems with Applications165, 113679 (2020).

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Expert Systems with Applications165, 113679 (2020)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:01.104779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28795e33-9c6f-4842-9252-002665f76f38 · outbound

This paper cites an unresolved cited work.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Unresolved cited work

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T23:28:05.977457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bdf7c4c1-dedc-46bc-9102-92c61e6a3388 · outbound

This paper cites In: Findings of the Association for Computational Linguistics (2022).

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Findings of the Association for Computational Linguistics (2022)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:08.515452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5071a609-cca4-4ca3-b5d0-3b14bef30c14 · outbound

This paper cites In: 2020 Fourth Int.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: 2020 Fourth Int

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-06T23:28:05.647151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5d3e4f1-a016-4c69-801a-c79fca4bdfaa · outbound

This paper cites ACM Computing Surveys55, 1–35 (2022).

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models ACM Computing Surveys55, 1–35 (2022)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:08.272304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de5233e1-0f5f-424d-9cb9-92452669a179 · outbound

This paper cites In: Proceedings of NAACL- HLT 2019, pp.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Proceedings of NAACL- HLT 2019, pp

Reference 6

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T23:28:08.058257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cf01d031-01a1-4e88-a5f7-e4d0359729c9 · outbound

This paper cites Leveraging BERT for Extractive Text Summarization on Lectures.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Leveraging BERT for Extractive Text Summarization on Lectures

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:01.724747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa8b90bb-4c5d-4e3a-90ea-dcff63d2288b · outbound

This paper cites In: Advances in Neural Information Processing Systems, vol.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Advances in Neural Information Processing Systems, vol

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:07.867182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation acad2887-7f5d-46fc-89f5-36bb178f421f · outbound

This paper cites Transactions of the ACL 12, 39–57 (2024).https://doi.org/10.1162/tacl_a_00632.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Transactions of the ACL 12, 39–57 (2024).https://doi.org/10.1162/tacl_a_00632

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:02.076740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c1cd3b3-6346-4441-93c1-7a621a1cd6bb · outbound

This paper cites In: Findings of the Association for Computational Linguistics: ACL 2023, pp.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Findings of the Association for Computational Linguistics: ACL 2023, pp

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:07.667017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eaf47196-0d8b-4445-a430-b844309ad462 · outbound

This paper cites Transactions of the ACL 12, 157–173 (2024).https://doi.org/10.1162/tacl_a_00638.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Transactions of the ACL 12, 157–173 (2024).https://doi.org/10.1162/tacl_a_00638

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:02.485168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b17fb8f-8465-4d3b-975e-797d4c3c17a4 · outbound

This paper cites BookSum: A Collection of Datasets for Long-form Narrative Summarization.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models BookSum: A Collection of Datasets for Long-form Narrative Summarization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:02.571808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a6cad7f-86ce-453c-9ded-38df178806c6 · outbound

This paper cites In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:07.467160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46c5ab66-611a-4154-85fb-f0888a63eed2 · outbound

This paper cites an unresolved cited work.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:28:07.250475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e6216b84-54eb-4eca-b0c8-5bf71ef13d15 · outbound

This paper cites The Chronicles of RAG: The Retriever, the Chunk and the Generator.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models The Chronicles of RAG: The Retriever, the Chunk and the Generator

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:02.926492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1aeedcf6-01b9-4a11-b637-96270e1c0441 · outbound

This paper cites IEEE Access11, 36120–36146 (2023).https: //doi.org/10.1109/ACCESS.2023.3266377.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models IEEE Access11, 36120–36146 (2023).https: //doi.org/10.1109/ACCESS.2023.3266377

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:03.047781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 608edd58-e958-4472-9ee2-65f4004951c4 · outbound

This paper cites Nomic Embed: Training a Reproducible Long Context Text Embedder.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Nomic Embed: Training a Reproducible Long Context Text Embedder

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:03.180723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5679c16-0151-4978-acde-e73f5de1dfe7 · outbound

This paper cites In: Proc.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Proc

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:03.284683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77de7d07-bab1-433e-9f07-8505fce0317c · outbound

This paper cites https://doi.org/10.1007/s00357-014-9161-z.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models https://doi.org/10.1007/s00357-014-9161-z

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:03.412916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b796a2c5-fc96-4604-8b2f-1cdf25dd159a · outbound

This paper cites Available at: https://openai.com/index/ gpt-4o-mini-advancing-cost-efficient-intelligence/ (2024).

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Available at: https://openai.com/index/ gpt-4o-mini-advancing-cost-efficient-intelligence/ (2024)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:07.084318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d36ec68-8152-4396-8d46-f91a7ce735c6 · outbound

This paper cites In: Algo- rithms and Computation, LNCS vol.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Algo- rithms and Computation, LNCS vol

Reference 21

Resolution: verified exact; doi, observed 2026-08-06T23:28:04.719358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e63f0a89-381c-4eea-8d44-b63a13020772 · outbound

This paper cites SIGACT News 28(2), 40–52 (1997).https://doi.org/10.1145/261342.571216.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models SIGACT News 28(2), 40–52 (1997).https://doi.org/10.1145/261342.571216

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:03.722119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3941db17-0c6c-40d6-a9ad-f18fdf8f8502 · outbound

This paper cites an unresolved cited work.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:28:06.917219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e404890f-648a-46ea-85dd-0a8bec7d56ab · outbound

This paper cites In: Text Summarization Branches Out, pp.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Text Summarization Branches Out, pp

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:06.689981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2d32600-46aa-4b0b-a3cf-f108cae00f8e · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models BERTScore: Evaluating Text Generation with BERT

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:04.155619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc049121-3107-42c6-bdd6-35e3affc50fb · outbound

This paper cites In: Proceedings of the 29th International Conference on Computational Linguistics, pp.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Proceedings of the 29th International Conference on Computational Linguistics, pp

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:06.510569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f40e1c3-3aec-464f-b4ac-0c921ff83dc4 · outbound

This paper cites In: Proceedings of the 58th Annual Meeting of the Association for Com- putational Linguistics, pp.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models In: Proceedings of the 58th Annual Meeting of the Association for Com- putational Linguistics, pp

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:06.211823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 202d2d90-b760-46d6-aa4f-3f308660064d · outbound

This paper cites an unresolved cited work.

Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models Unresolved cited work

Reference 5255

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:02.383058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4dc2bad5-597b-4a54-b856-a8b2916e6f24 · inbound

Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation cites this paper.

Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T00:29:59.970446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.