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Paper Citation Record · LEDGER

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization

As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2606.05494.

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

pith.paper-citation-record.v1
2606.05494 v4

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:24:51.912868Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

26 of 26 outbound references displayed

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Outbound references

Observation 2343320c-ab9d-4446-ac80-b254e1cc17d1 · outbound

This paper cites Summn: A multi-stage summarization frame- work for long input dialogues and documents,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Summn: A multi-stage summarization frame- work for long input dialogues and documents,

Reference 1

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Observation a3a1d777-4f0b-4db1-b47b-a65436912897 · outbound

This paper cites Chatgpt vs human-authored text: Insights into controllable text summarization and sentence style transfer,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Chatgpt vs human-authored text: Insights into controllable text summarization and sentence style transfer,

Reference 2

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source=pdf_text observed=2026-08-02T12:24:51.784973Z digest=sha256:697eb891280f2c75aa2d3f0ad0764150c11d30be138315656476cc53c9909b06

Observation 8e9dd83d-e691-4d0f-bcd3-318e4734136b · outbound

This paper cites Domain adaptation with pre-trained transformers for query-focused abstractive text summariza- tion,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Domain adaptation with pre-trained transformers for query-focused abstractive text summariza- tion,

Reference 3

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Observation 48e95cc0-9f37-45c8-9dad-8695c50bd092 · outbound

This paper cites Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models

Reference 4

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source=pdf_text observed=2026-08-02T12:24:51.795113Z digest=sha256:110222c0571e5ae0231cea413618e35b1335aa0201eb60677031b7249529d8ab

Observation a8a836e5-3a8c-49b3-84f6-6ebc7dbd1c1a · outbound

This paper cites Abstractive meeting summarization: A survey,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Abstractive meeting summarization: A survey,

Reference 5

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source=pdf_text observed=2026-08-02T12:24:51.800930Z digest=sha256:a45860d8f32b2f11ecb5ce696213f2b26b56d70a2d7323604692ad96d3f78160

Observation 8c8b3be5-ebd4-4be6-b7f0-ad3a9f85bbff · outbound

This paper cites Anlirika: An LSTM–CNN flow twister for spoken language identification,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Anlirika: An LSTM–CNN flow twister for spoken language identification,

Reference 6

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source=pdf_text observed=2026-08-02T12:24:51.806865Z digest=sha256:3235bb6d03e9b0ea9fd32b50a66512ff3fdc34e0e352d3c7758675bacb7240f4

Observation d64bbcd5-fdd8-4342-bf30-390bf40d2b07 · outbound

This paper cites Riro: Reshaping inputs, refining outputs unlocking the potential of large language models in data-scarce contexts,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Riro: Reshaping inputs, refining outputs unlocking the potential of large language models in data-scarce contexts,

Reference 7

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source=pdf_text observed=2026-08-02T12:24:51.815420Z digest=sha256:2749065865a63f98c8ad9bc277887c4d3434199958c6d50e10292f1f9b0c107c

Observation ce5aa72b-84f2-49cf-8b93-278056595c8e · outbound

This paper cites Au- tomatic text summarization: A comprehensive survey,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Au- tomatic text summarization: A comprehensive survey,

Reference 8

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source=pdf_text observed=2026-08-02T12:24:51.820767Z digest=sha256:64969c0666e85744c61a6aa5f434d235c1141481cbfaaca85c03902001b9e811

Observation 40b025cd-d05a-4e0a-9d3c-3caf29ca2b30 · outbound

This paper cites A comprehensive survey on automatic text summarization with exploration of llm-based methods,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization A comprehensive survey on automatic text summarization with exploration of llm-based methods,

Reference 9

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source=pdf_text observed=2026-08-02T12:24:51.826401Z digest=sha256:b136a7bbc62f4cb89314b36b747dd5c5c0f33a7df16cfcaebaa9109286b80c79

Observation 19e78a96-636e-4742-9ac5-2928a4b12c20 · outbound

This paper cites A survey of automatic text summarization using graph neural networks,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization A survey of automatic text summarization using graph neural networks,

Reference 10

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source=pdf_text observed=2026-08-02T12:24:51.831592Z digest=sha256:0ee4488133a4f03869a1e1896c8d4622fe8cef1a4a9b3c6de836722d8b8ba25e

Observation 054764f1-116f-4425-bc52-c4066f7b4c5b · outbound

This paper cites Balancing factual con- sistency and diversity in abstractive summarization via model-agnostic composite reranking,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Balancing factual con- sistency and diversity in abstractive summarization via model-agnostic composite reranking,

Reference 11

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source=pdf_text observed=2026-08-02T12:24:51.836808Z digest=sha256:e012e78e608eb7c5f9fe1c54c4248f984c0f417bc4ca5785c9a9cdc41c1e0810

Observation b03cc811-bfb3-46ca-9c96-5421fdb29467 · outbound

This paper cites Abstractive text summarization: State of the art, challenges, and improvements,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Abstractive text summarization: State of the art, challenges, and improvements,

Reference 12

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source=pdf_text observed=2026-08-02T12:24:51.841666Z digest=sha256:9b4bc86d25b4dba8e1dd9d94937a3daa15cd93c414dbc4da68321f23b73096f8

Observation 472d27b4-511a-40c9-9baf-e597eb7bbb16 · outbound

This paper cites Lexisem: A re- ranker balancing lexical and semantic quality for enhanced abstractive summarization,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Lexisem: A re- ranker balancing lexical and semantic quality for enhanced abstractive summarization,

Reference 13

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Observation 58aba479-86ed-4208-8c8c-de166713a49d · outbound

This paper cites Automatic text summarization using soft- cosine similarity and centrality measures,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Automatic text summarization using soft- cosine similarity and centrality measures,

Reference 14

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source=pdf_text observed=2026-08-02T12:24:51.852119Z digest=sha256:8aaa399c2fe76e7ae96de0b3dadfd0f27440a36ecf9f4d4982c29e3a30b6e84a

Observation 30181438-1d53-4374-b7ac-a82f5ca250fd · outbound

This paper cites Ranksum—an unsupervised extractive text summarization based on rank fusion,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Ranksum—an unsupervised extractive text summarization based on rank fusion,

Reference 15

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source=pdf_text observed=2026-08-02T12:24:51.857210Z digest=sha256:cbf76b41666bff6d98c151e42e47d331876be1ef3984ff2b8390715898a413bf

Observation 500d2ab3-d761-4b7d-8bad-bce0c3cd0c26 · outbound

This paper cites Extractive summarization using extended TextRank algorithm,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Extractive summarization using extended TextRank algorithm,

Reference 16

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Observation b09cf823-fe9c-43d6-bd0e-b506a03886f3 · outbound

This paper cites A topic modeled un- supervised approach to single document extractive text summarization,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization A topic modeled un- supervised approach to single document extractive text summarization,

Reference 17

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source=pdf_text observed=2026-08-02T12:24:51.867776Z digest=sha256:c5f0c1dedbacaa13781136a10a2ed74b8b3c08eb83ef2d6b6cb5d7ded26d2cb7

Observation 54cd8f13-a166-4b4d-870b-85620bdf211e · outbound

This paper cites Bidirectional lstm networks for abstractive text summarization,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Bidirectional lstm networks for abstractive text summarization,

Reference 18

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source=pdf_text observed=2026-08-02T12:24:51.872764Z digest=sha256:96666294ae8fa0eaf8d30a5fd5efea24f11e9606dcb1d0d37d4cbb9736328af8

Observation 4f838626-55e5-4038-8496-bda8cc2ffaa8 · outbound

This paper cites Summfactscore: A claim-centric framework forreference-free factual consistency evalu- ation inlong-document summarization,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Summfactscore: A claim-centric framework forreference-free factual consistency evalu- ation inlong-document summarization,

Reference 19

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source=pdf_text observed=2026-08-02T12:24:51.877629Z digest=sha256:7b3cc8d5017c5d66073993d52683ca289d22699c7c8fba491e5d710216875140

Observation 49adb2aa-b14a-4793-85dc-12f118397b6e · outbound

This paper cites Discourse-aware neural extractive text summarization,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Discourse-aware neural extractive text summarization,

Reference 20

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source=pdf_text observed=2026-08-02T12:24:51.882639Z digest=sha256:c6db1ddda469a5ced32584784816cac32d380a753c0ec1ac5c5b54efbf2feaf1

Observation 6b918c14-6e51-4e99-ae4e-627c323b9b3b · outbound

This paper cites Deep learning for text summarization using nlp for automated news digest,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Deep learning for text summarization using nlp for automated news digest,

Reference 21

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Observation 22214d2a-256c-464b-abb5-9151304e3ed9 · outbound

This paper cites Extractive summarization as text matching,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Extractive summarization as text matching,

Reference 22

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source=pdf_text observed=2026-08-02T12:24:51.893179Z digest=sha256:d862211fa4484a89a6d020fa8d5433366c801a994af4e1b11b9681f742055973

Observation 882c30e2-2e92-4d2b-80c4-1353f76578bb · outbound

This paper cites Adapted large language models can outperform medical experts in clinical text summarization,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Adapted large language models can outperform medical experts in clinical text summarization,

Reference 23

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source=pdf_text observed=2026-08-02T12:24:51.898202Z digest=sha256:49fdc7de5a212f7f1abc47cde3daf65e7c2120cfee74a4094e43cdfa9907b273

Observation a49c36f0-cf3a-4c0b-9641-6ac66be828ff · outbound

This paper cites A survey on cross-lingual summarization,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization A survey on cross-lingual summarization,

Reference 24

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source=pdf_text observed=2026-08-02T12:24:51.903028Z digest=sha256:e75a71c95fba230043427ea1444744402313f79e618877f26b506faa0d60e40d

Observation 750694ee-8c26-4bd1-b85f-63cd770a0e9f · outbound

This paper cites News Summarization and Evaluation in the Era of GPT-3.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization News Summarization and Evaluation in the Era of GPT-3

Reference 25

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Observation 4115ab0b-3c7c-4a1a-a7c3-07459be3eaf2 · outbound

This paper cites Damb: A dynamic adaptive multi-model benchmarking framework for abstractive text summarization,.

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization Damb: A dynamic adaptive multi-model benchmarking framework for abstractive text summarization,

Reference 26

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Pith citing papers

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