Pith. sign in

Paper Citation Record · LEDGER

Meta-Transfer Learning for Low-Resource Abstractive Summarization

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2102.09397.

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

pith.paper-citation-record.v1
2102.09397 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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:35:05.971047Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T14:59:10.469562Z

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 24b752b0-30fe-49f0-bb36-b143f4328308 · inbound

State Space Models for Extractive Summarization in Low Resource Scenarios cites this paper.

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:aa9d1ccd9c6f1fae41f48086bdf11b43881e2fc9f945eeaa95d316d368e2788e

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

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

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.

source=arxiv_source observed=2026-08-15T22:35:05.971047Z digest=sha256:1dc83141b05bd6eb85a50c0fb516aaa12c3a27881181b00cfd94ffb07442fe91