Pith. sign in

Paper Citation Record · LEDGER

Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2004.03974.

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

pith.paper-citation-record.v1
2004.03974 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:43:26.050736Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T17:51:45.782104Z

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 db2ecd05-6d36-46e3-928b-290e4415f4e1 · inbound

Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling cites this paper.

Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T05:43:26.050736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:43:26.050736Z digest=sha256:d56df43d621afabdc785c8eac550fa0c3cb7a3c5bddd534995b344c5535dbe81

Observation b2e027a6-f609-4680-98af-bcacaee85a22 · inbound

NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation cites this paper.

NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T16:36:24.443460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:36:24.443460Z digest=sha256:b2e19fb7cf09e3af55294b1cb9121fe4c4eb6dc85726e1c405b1ab6bd4980b90

Observation c8fd08f7-8b55-4a49-b179-62e2c4f8c7a7 · inbound

Continual Neural Topic Model cites this paper.

Continual Neural Topic Model Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

Reference 2017

Resolution
metadata mismatch
local_arxiv, observed 2026-08-05T17:51:45.826214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T17:51:45.229914Z digest=sha256:bc31a8a395d366578c65c5e30258d8f1f8e878522dab9733043c25429dd524ab