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Paper Citation Record · LEDGER

Repurposing Decoder-Transformer Language Models for Abstractive Summarization

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1909.00325.

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

pith.paper-citation-record.v1
1909.00325 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:59:39.677951Z

measured 33 of 33 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

33 of 33 outbound references displayed

  • verified exact2
  • verified fuzzy1
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3fa0ea9-aca1-43d6-b545-e6db3e0e8300 · outbound

This paper cites URL: " 'urlintro :=.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization URL: " 'urlintro :=

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.554935Z digest=sha256:a1338ae9c3e4889714e59c89e85b75041298607ea263bbb029792637da1dad6c

Observation eea6d6f5-75ab-4672-9e00-faf689d5db08 · outbound

This paper cites write newline.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization write newline

Reference 2

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no resolver link, observed 2026-08-14T05:59:39.560273Z

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source=arxiv_source observed=2026-08-14T05:59:39.560273Z digest=sha256:a08893f5367e13f783b5aa12a569bdcf6cc46f1decb766086dbe164d07390fa0

Observation 118cee23-5e0d-4fe1-99fe-468c6e8f3a89 · outbound

This paper cites Deep Communicating Agents for Abstractive Summarization.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Deep Communicating Agents for Abstractive Summarization

Reference 3

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source=arxiv_source observed=2026-08-14T05:59:39.564270Z digest=sha256:dfbadd8ac4e28583a911e67325bed429d85f478a0c8186d29158014515270911

Observation 5f928dc8-e101-44e8-a12f-4221e0231a33 · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 4

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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=arxiv_source observed=2026-08-14T05:59:39.568644Z digest=sha256:d6adc038c2d1c6acfcb3f98698bdd1bb12d3580799e5b96ce6e55ca61aecc767

Observation 03114911-13f7-424a-bf3d-1d90e01e71d2 · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 5

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.572494Z digest=sha256:a4dbea30748490053d7ecd836d45b2085f801af2bf441b8f30928b34b573b5fd

Observation c5f8db10-c982-4da8-ae7e-e1621255623a · outbound

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

Repurposing Decoder-Transformer Language Models for Abstractive Summarization BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.576246Z digest=sha256:cee8976b2a5b9b991242b03fad36a9b1d8c78c3227f1e7dabd36b440a3e73d1f

Observation b5ab106c-35d4-4682-bb31-a3102f85ad2a · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 7

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no resolver link, observed 2026-08-14T05:59:39.580046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.580046Z digest=sha256:d8a70da47d60def1aa9f4ec3929e6c426e6c94a9167aae997e9121f2646fc390

Observation c5b3c5fd-e1e4-4f2c-ad16-db48d3a3ec07 · outbound

This paper cites Incorporating Copying Mechanism in Sequence-to-Sequence Learning.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Incorporating Copying Mechanism in Sequence-to-Sequence Learning

Reference 8

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source=arxiv_source observed=2026-08-14T05:59:39.583537Z digest=sha256:3c5840aedbf83e67cb1acc63b21234242a2f6d448b0ece00e9d0c7b7d85d2fa4

Observation ee7d3775-6788-489a-82a1-15bd2e505e03 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization The Curious Case of Neural Text Degeneration

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.587768Z digest=sha256:ae9716ded59acf6d427d1bf61dbf5081bff9165e0b0ec3dfb4d8e6694d9c1b2e

Observation e4a86387-42b1-42b6-9d44-ceb5df58f635 · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.591532Z digest=sha256:5e42141c188f710b1a9286037731b8933e5dc29c77abfcc6637e28ac6646fbe1

Observation 6b16ed9e-1594-43d2-a528-8ef61f33cb81 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Adam: A Method for Stochastic Optimization

Reference 11

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.594984Z digest=sha256:059d36285cc2ac8ca3a62deb34303a34066e390b9585b8b400c7f1a3adc3f42d

Observation 93914926-9609-4d0a-9eb3-242293e6df2f · outbound

This paper cites SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.599082Z digest=sha256:89f7302f194c03964e32908fc5a1cc1fd7a6117ce1a6d5e15a6d5e3850125f7f

Observation e9e63412-78f1-4ca8-8f13-5beaa88213ad · outbound

This paper cites Layer Normalization.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Layer Normalization

Reference 13

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no resolver link, observed 2026-08-14T05:59:39.602861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.602861Z digest=sha256:db5b897054e0c6cee124ca27480b5886ca00259bad7f57a63114b244812ecb97

Observation d8142070-7843-4e71-8a57-b0b5a048d637 · outbound

This paper cites Actor-Critic based Training Framework for Abstractive Summarization.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Actor-Critic based Training Framework for Abstractive Summarization

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.606319Z digest=sha256:6aaaf68ff0cac61add42377a406e7cb78189f24129a211037e270a67a9714773

Observation 8c6e6563-9989-4f31-92ff-4bd8ae1cec4d · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 15

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raw_fallback, observed 2026-08-14T05:59:39.923925Z

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=arxiv_source observed=2026-08-14T05:59:39.610073Z digest=sha256:79816839cc08b0208bb53a17ab2e5c5f969b4db904856a74fe66981c72cb6a86

Observation 2b3bf7d9-dfd7-404f-85f4-9cd95c1b2966 · outbound

This paper cites Liu, Mohammad Ahmad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Liu, Mohammad Ahmad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-14T05:59:39.913260Z

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=arxiv_source observed=2026-08-14T05:59:39.613846Z digest=sha256:fa10aa28c330d740d57b2f70089a87e6a7c18486b3dd1874b832325369dcc35f

Observation 2dce94d5-fb15-4324-bb05-e34a077af28d · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 17

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verified exact
doi, observed 2026-08-14T05:59:39.708932Z

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=arxiv_source observed=2026-08-14T05:59:39.617785Z digest=sha256:001cfb1ecc8ad8c876f92c5c0c69dabee0c5f955943b6e839b90a5de21488413

Observation 35e06056-7c0b-4ff2-bf1b-2d01cf20d88c · outbound

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

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.622038Z digest=sha256:1d12eeabbb9344d2ed341249dc6fcfc919005aa969f28d3e7e963ce7f39fa406

Observation 512f626f-c8fc-458b-a021-3a178fadfe1d · outbound

This paper cites Cohen, and Mirella Lapata.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Cohen, and Mirella Lapata

Reference 19

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source=arxiv_source observed=2026-08-14T05:59:39.626476Z digest=sha256:eef1a771f84d8ee551a81bbdf188c8ee162bca1606eaf04c774e96c6b517266f

Observation 7985c6c1-79ad-4cb5-ac66-52a927bfffad · outbound

This paper cites A Deep Reinforced Model for Abstractive Summarization.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization A Deep Reinforced Model for Abstractive Summarization

Reference 20

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no resolver link, observed 2026-08-14T05:59:39.630755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.630755Z digest=sha256:87b284828c1c2dde89fa2827c632075e66ff0da31c38e6f0aaaabdf10721463c

Observation e976b93c-3201-411f-8ca2-d5896e86c67b · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 21

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.634628Z digest=sha256:93c1f732de08da3ef52050359fa7eb5c2eea44eeed5938f0d901e5da79fe1f3a

Observation bfd5d0b9-06cc-4db1-a49b-90d5521c4b92 · outbound

This paper cites Improving language understanding by generative pre-training.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Improving language understanding by generative pre-training

Reference 22

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.638711Z digest=sha256:da0e7d9ae458772687ee7b08f1990de598d992615ea8a9d1eff3a5369ae9219b

Observation 95b86dea-bd6a-416d-b99f-b815d9f7e87d · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 23

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source=arxiv_source observed=2026-08-14T05:59:39.642668Z digest=sha256:3fcf500f23bc338adf31443833e496fe2b49f82b21c9683c7cb698a3a40dccb4

Observation 9dbdec1b-e931-4fef-9034-9e58b9fc1f30 · outbound

This paper cites A Neural Attention Model for Abstractive Sentence Summarization.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization A Neural Attention Model for Abstractive Sentence Summarization

Reference 24

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source=arxiv_source observed=2026-08-14T05:59:39.645820Z digest=sha256:be4b111478ecd4e4ca87ca31ab5bb76d79b2738e62dbb72f049fea5b40e98c6f

Observation 53035531-eca7-4982-ac35-4430a1370341 · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 25

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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=arxiv_source observed=2026-08-14T05:59:39.649315Z digest=sha256:bbbfd6ef3de9a37656225f1cf2191bcf37e1c5dc49ac36c1c8231999d5d31da7

Observation 5d6ed72b-ccd7-4834-9fab-cfb0078b55ba · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 26

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no resolver link, observed 2026-08-14T05:59:39.653191Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.653191Z digest=sha256:de7f46a9e6cbcdf5cf09f600eb460ccf363d95f4f9f663ad9f5a5c6b2aa050d4

Observation aff0ecfc-46ba-44e4-8cb9-1d03a5ffb015 · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Neural Machine Translation of Rare Words with Subword Units

Reference 27

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.656661Z digest=sha256:9ce959a64d0f1adf9789c2dab2c0b185450a390abf9b9da7be4192d0ec0eebe7

Observation 3ceadbe9-6f0a-42f9-9222-f990f7d78223 · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 28

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no resolver link, observed 2026-08-14T05:59:39.660573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.660573Z digest=sha256:67d3405478fc674f8cc3c38be1baba0d0ddef1dee423e2ecf9801f78c2f8bc39

Observation 99421633-5061-4821-9442-9f7a34f7584a · outbound

This paper cites Modeling Coverage for Neural Machine Translation.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Modeling Coverage for Neural Machine Translation

Reference 29

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no resolver link, observed 2026-08-14T05:59:39.663778Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.663778Z digest=sha256:fa05be54535f6b206b27b9910401b9a796a7cb34ec7a05d72884b5d2b41398ce

Observation 74317759-001f-4c56-80c7-31a6e65ba0cc · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 30

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unresolved
no resolver link, observed 2026-08-14T05:59:39.667610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.667610Z digest=sha256:e22285f7302d6bff2d1a5dff3d6d907e3c5f40b682a53ef4672daf9a9c2f5201

Observation 34fc66e7-82a5-4581-8600-d37b5e18b7fa · outbound

This paper cites an unresolved cited work.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Unresolved cited work

Reference 31

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no resolver link, observed 2026-08-14T05:59:39.671378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.671378Z digest=sha256:4be04d04abf2903a89965160684942c6e45886f8907a4fdd8c92b34261c8787d

Observation 556b5903-a98b-4967-ad31-a33bab998cfa · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 32

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no resolver link, observed 2026-08-14T05:59:39.674676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:59:39.674676Z digest=sha256:99392685888a555f44f90a3bb0ce57659fd9aa4e20cc3aa9352adc4fdf2ac154

Observation 5089fa09-b005-4bdc-9518-906389c4e046 · outbound

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

Repurposing Decoder-Transformer Language Models for Abstractive Summarization Efficient Summarization with Read-Again and Copy Mechanism

Reference 33

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verified exact
local_arxiv, observed 2026-08-14T05:59:39.724024Z

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=arxiv_source observed=2026-08-14T05:59:39.677951Z digest=sha256:23861a0fc1d6a2c9d34ba05bc4f408f7fda53a780d26018deef11e9e7651dfec

Pith citing papers

No inbound Pith citation observations are available.