Paper Citation Record · LEDGER

Fine-tune BERT for Extractive Summarization

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1903.10318.

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

pith.paper-citation-record.v1
1903.10318 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:44:02.810885Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T13:26:58.790495Z

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 a095a61f-1dec-4156-8212-816604311741 · inbound

What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks cites this paper.

What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks Fine-tune BERT for Extractive Summarization

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-24T18:59:49.553121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f9483a93-d706-4173-821e-846944f428bf · inbound

Enriching and Controlling Global Semantics for Text Summarization cites this paper.

Enriching and Controlling Global Semantics for Text Summarization Fine-tune BERT for Extractive Summarization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-24T13:56:12.866301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb01923c-869e-4b37-8c4f-d205924da3a7 · inbound

TalkLess: Blending Extractive and Abstractive Speech Summarization for Editing Speech to Preserve Content and Style cites this paper.

TalkLess: Blending Extractive and Abstractive Speech Summarization for Editing Speech to Preserve Content and Style Fine-tune BERT for Extractive Summarization

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T15:44:02.810885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d14552a-d821-4e7c-82aa-b29508f493ae · inbound

EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents cites this paper.

EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents Fine-tune BERT for Extractive Summarization

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:26:58.791873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cf454560-4208-405e-b4c3-6156320fac07 · inbound

A Tree-of-Thoughts Inspired Hybrid Approach for Legal Case Judgement Summarization using LLMs cites this paper.

A Tree-of-Thoughts Inspired Hybrid Approach for Legal Case Judgement Summarization using LLMs Fine-tune BERT for Extractive Summarization

Reference 129

Resolution: verified exact; arxiv_id, observed 2026-07-01T17:05:50.420868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2017d7e4-4205-45de-bdf3-587c3027dc79 · inbound

AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System cites this paper.

AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System Fine-tune BERT for Extractive Summarization

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T14:17:27.928663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.