Paper Citation Record · LEDGER

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting

As of 17 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2505.06862.

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

pith.paper-citation-record.v1
2505.06862 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:35:06.079754Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

27 of 27 outbound references displayed

verified exact3
verified fuzzy1
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 786f5f65-1214-4f59-9ca1-4d033c25eb16 · outbound

This paper cites Meta-Transfer Learning for Low-Resource Abstractive Summarization.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Meta-Transfer Learning for Low-Resource Abstractive Summarization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:05.971047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07965a8e-0704-46ad-b842-c0b0e5c6ae56 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Generating Long Sequences with Sparse Transformers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:05.975584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1358a719-c0aa-4c0c-a587-148b3c4a5421 · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 3

Resolution: verified exact; doi, observed 2026-08-15T22:35:06.176530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2aed70a2-a4d2-4ce0-ba1c-8afc2776725a · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:05.984586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b60b52b4-25ce-4347-ab23-80be7b6bb36b · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:05.988538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d5b11c7-b560-4bf5-b644-68ee38864bf1 · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:35:06.511083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 91378cfc-e00c-4a73-871a-50c33addc3f9 · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 7

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T22:35:06.447493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b142c41e-b56c-49c1-9beb-a3eb0de83948 · outbound

This paper cites Efficient Attentions for Long Document Summarization.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Efficient Attentions for Long Document Summarization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.000675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 953f671d-6cdc-4105-8c20-d4ff03f6e6c7 · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 9

Resolution: verified exact; doi, observed 2026-08-15T22:35:06.155965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f996817f-76e8-487e-b7ac-40440ce13d44 · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.009851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecbab4e6-86ab-457d-9620-ddc27589e24c · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.014027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d60dd244-15ce-4b7f-a648-b182f179ddb7 · outbound

This paper cites Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.018573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 854ad310-e7c1-4190-b984-d46a6a10eb16 · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.022609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 701de81e-5ea4-4603-91a3-5c9900dcf5e9 · outbound

This paper cites Improving Language Understanding by Generative Pre - Training.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Improving Language Understanding by Generative Pre - Training

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:06.498543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d6459da8-3d65-4968-ab0e-e6de6d39fa39 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.030417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25b746ae-a380-4e86-9eca-1f4cdeed9f52 · outbound

This paper cites Rush, Sumit Chopra, and Jason Weston.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Rush, Sumit Chopra, and Jason Weston

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.034503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e766f171-15bc-45d4-aa66-2bac42905bc1 · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 17

Resolution: verified exact; doi, observed 2026-08-15T22:35:06.127924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aa3796c5-950f-447d-a55f-323cf63111a5 · outbound

This paper cites Liu, and Christopher D.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Liu, and Christopher D

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.043087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6170fe7a-072e-4b5b-8683-7690ae4f9dcf · outbound

This paper cites BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.047144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc8f2b39-958f-45cc-aaa6-4e71a5eb0965 · outbound

This paper cites Sequence to Sequence Learning with Neural Networks.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Sequence to Sequence Learning with Neural Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.051276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3365c613-7451-424f-adc7-549b3745c734 · outbound

This paper cites Attention Is All You Need.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Attention Is All You Need

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.055226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f1704ee-5c7b-4e6d-9a81-8a834c0e037b · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.059360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ccb145-c0bd-45f5-a7ac-ce4e1858e105 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.063341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96df954d-eb57-4e94-a424-ee0d182f548a · outbound

This paper cites Big Bird: Transformers for Longer Sequences.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Big Bird: Transformers for Longer Sequences

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.067261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dada56ee-6a47-47b9-b9dd-24eca79f5781 · outbound

This paper cites Efficient Summarization with Read-Again and Copy Mechanism.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Efficient Summarization with Read-Again and Copy Mechanism

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.071283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f7eb164-08b9-4151-a0ca-2f4b9f79ac45 · outbound

This paper cites PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.075681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2bf3d8e-21b5-420d-9983-29482d1a8650 · outbound

This paper cites an unresolved cited work.

A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource Setting Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:06.079754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.