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Paper Citation Record · LEDGER

State Space Models for Extractive Summarization in Low Resource Scenarios

As of 18 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:2501.14673.

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

pith.paper-citation-record.v1
2501.14673 v1

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:59:10.353069Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

8 of 8 outbound references displayed

  • verified exact2
  • verified fuzzy1
  • unresolved1
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24b752b0-30fe-49f0-bb36-b143f4328308 · outbound

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

State Space Models for Extractive Summarization in Low Resource Scenarios Meta-Transfer Learning for Low-Resource Abstractive Summarization

Reference 3

Resolution
metadata mismatch
local_arxiv, observed 2026-08-10T14:59:10.474774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-10T14:59:10.321547Z digest=sha256:40a1d56ad95d2b08be67d00f6872cf62e53d66ce14c2afd202102d41552b6bbf

Observation 427b926f-1ec7-4fd7-a507-3670296d32e6 · outbound

This paper cites On Optimal Transformer Depth for Low-Resource Language Translation.

State Space Models for Extractive Summarization in Low Resource Scenarios On Optimal Transformer Depth for Low-Resource Language Translation

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-08-10T14:59:10.452670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-10T14:59:10.326295Z digest=sha256:1a694869204c6241a750436eac33d5f35e67b2b6e437ac58980357879ae6386c

Observation ecb3e246-88b8-4a1a-aa91-393f446b108c · outbound

This paper cites In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871 –7880, Online.

State Space Models for Extractive Summarization in Low Resource Scenarios In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871 –7880, Online

Reference 5

Resolution
malformed identifier
no resolver link, observed 2026-08-10T14:59:10.332817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:59:10.332817Z digest=sha256:a8976fc61b0c95071b111aea298f884dcab4b7d613252e6261329ae7800601e8

Observation 7333819e-54f2-4e80-ac4b-00a6d5945bd4 · outbound

This paper cites In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4143 –4159, Online.

State Space Models for Extractive Summarization in Low Resource Scenarios In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4143 –4159, Online

Reference 7

Resolution
verified exact
doi, observed 2026-08-10T14:59:10.404451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-10T14:59:10.343002Z digest=sha256:03c2c9ff8f2e45783bb771aea7ac3f77f2518243f04504d571719412c906cf42

Observation 6f45fd7e-5471-4bd8-9418-631196f7122c · outbound

This paper cites In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5892 –5904, Online.

State Space Models for Extractive Summarization in Low Resource Scenarios In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5892 –5904, Online

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:59:10.491317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-10T14:59:10.347968Z digest=sha256:606e04d258c422ee5d83d72b669b88465355160d5310df4ec1696cbeda3247ba

Observation ea56bcfe-058e-4a58-bb36-d2b507295371 · outbound

This paper cites an unresolved cited work.

State Space Models for Extractive Summarization in Low Resource Scenarios Unresolved cited work

Reference 2019

Resolution
verified exact
doi, observed 2026-08-10T14:59:10.388074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-10T14:59:10.353069Z digest=sha256:b6d6f1ee7bf5ae95237bfdec719f70557441bc8c06fac5756f0559608fdc79bc

Observation d17dbc69-db53-49ac-a474-89ee7d4b4c37 · outbound

This paper cites an unresolved cited work.

State Space Models for Extractive Summarization in Low Resource Scenarios Unresolved cited work

Reference 2021

Resolution
unresolved
raw_fallback, observed 2026-08-10T14:59:10.507660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-10T14:59:10.311562Z digest=sha256:811a9fd52a44f53f8718ea70241350114bc6487040c1f5e0baf1f116a85f46ec

Observation ee1e92d6-a620-4247-9c7d-1865650af4d8 · outbound

This paper cites Poincar\'e Embeddings for Learning Hierarchical Representations.

State Space Models for Extractive Summarization in Low Resource Scenarios Poincar\'e Embeddings for Learning Hierarchical Representations

Reference 2022

Resolution
malformed identifier
no resolver link, observed 2026-08-10T14:59:10.338045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:59:10.338045Z digest=sha256:562fc9210fe4efea5f5ca5ce8cc316eafed56716238af3fe3736194ae02324d2

Pith citing papers

No inbound Pith citation observations are available.