Decoupling Generation and Selection for Budget-Constrained Faithful Summarization | Paper Citation Record

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This paper cites Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling

Reference 1

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This paper cites BART : Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization BART : Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 2

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization 2026 , eprint=

Reference 3

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization S im CLS : A Simple Framework for Contrastive Learning of Abstractive Summarization

Reference 4

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization BRIO : Bringing Order to Abstractive Summarization

Reference 5

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This paper cites Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality

Reference 6

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This paper cites S umma R eranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization S umma R eranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization

Reference 7

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization 2025 , eprint=

Reference 8

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization 2026 , howpublished =

Reference 9

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization 2024 , url =

Reference 10

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Abstractive Text Summarization using Sequence-to-sequence RNN s and Beyond

Reference 11

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This paper cites Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model

Reference 12

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization ROUGE : A Package for Automatic Evaluation of Summaries

Reference 13

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization 2020 , eprint=

Reference 14

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Evaluating the Factual Consistency of Abstractive Text Summarization

Reference 15

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization M ini C heck: Efficient Fact-Checking of LLM s on Grounding Documents

Reference 16

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization A lign S core: Evaluating Factual Consistency with A Unified Alignment Function

Reference 17

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization F act KB : Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge

Reference 18

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This paper cites PRIMERA : Pyramid-based Masked Sentence Pre-training for Multi-document Summarization.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization PRIMERA : Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

Reference 19

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Benchmarking Large Language Models for News Summarization

Reference 20

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization On Context Utilization in Summarization with Large Language Models

Reference 21

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This paper cites Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias

Reference 22

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Discrete Mathematics, Algorithms and Applications , volume=

Reference 23

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization On Learning to Summarize with Large Language Models as References

Reference 24

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization

Reference 25

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Exploring Iterative Controllable Summarization with Large Language Models

Reference 26

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization and Marshall, Iain J

Reference 27

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This paper cites Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles

Reference 28

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Tell me what I need to know: Exploring LLM -based (Personalized) Abstractive Multi-Source Meeting Summarization

Reference 29

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Structure-Aware Chunking for Abstractive Summarization of Long Legal Documents

Reference 30

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization CCS um: A Large-Scale and High-Quality Dataset for Abstractive News Summarization

Reference 31

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Extractive Summarization with Text Generator

Reference 32

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Extract, Select and Rewrite: A Modular Sentence Summarization Method

Reference 33

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization On Faithfulness and Factuality in Abstractive Summarization

Reference 34

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Asking and Answering Questions to Evaluate the Factual Consistency of Summaries

Reference 35

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization QAF act E val: Improved QA -Based Factual Consistency Evaluation for Summarization

Reference 36

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Evaluating the Factual Consistency of Large Language Models Through News Summarization

Reference 37

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This paper cites Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency

Reference 38

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization F aith B ench: A Diverse Hallucination Benchmark for Summarization by M odern LLM s

Reference 39

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This paper cites On Positional Bias of Faithfulness for Long-form Summarization.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization On Positional Bias of Faithfulness for Long-form Summarization

Reference 40

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Dynamic Attention-Guided Context Decoding for Mitigating Context Faithfulness Hallucinations in Large Language Models

Reference 41

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information

Reference 42

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Hallucination Diversity-Aware Active Learning for Text Summarization

Reference 43

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This paper cites MAMM -Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization MAMM -Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration

Reference 44

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization 1998 , isbn =

Reference 45

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization A Scalable Global Model for Summarization

Reference 46

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization A Class of Submodular Functions for Document Summarization

Reference 47

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Foundations and Trends in Machine Learning , volume =

Reference 48

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Lightweight reranking for language model generations

Reference 49

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Reranking-based Generation for Unbiased Perspective Summarization

Reference 50

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Observation 96a726b5-6c57-467e-94c2-651c301d4193 · outbound

This paper cites Submodular-based In-context Example Selection for LLM s-based Machine Translation.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Submodular-based In-context Example Selection for LLM s-based Machine Translation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:17:12.795977Z

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This paper cites An End-to-End Submodular Framework for Data-Efficient In-Context Learning.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization An End-to-End Submodular Framework for Data-Efficient In-Context Learning

Reference 52

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization SMART : Submodular Data Mixture Strategy for Instruction Tuning

Reference 53

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization BERTScore: Evaluating Text Generation with BERT

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T15:17:12.400877Z

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Decoupling Generation and Selection for Budget-Constrained Faithful Summarization Journal of social computing , volume=

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:17:12.787454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7eefd3b6-8baf-446a-bacf-77eea208ce4e · outbound

This paper cites 2020 , editor =.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization 2020 , editor =

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T15:17:12.406680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec7dae35-2e3e-488c-b4cc-699f32c5f5e5 · outbound

This paper cites FABLES: Evaluating faithfulness and content selection in book-length summarization.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization FABLES: Evaluating faithfulness and content selection in book-length summarization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T15:17:12.409509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8f6b331-f44e-45a9-aedf-7b2ddacc02a1 · outbound

This paper cites T ofu E val: Evaluating Hallucinations of LLM s on Topic-Focused Dialogue Summarization.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization T ofu E val: Evaluating Hallucinations of LLM s on Topic-Focused Dialogue Summarization

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T15:17:12.413049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dc41cfd-14a7-41d1-9920-ce46e84242fd · outbound

This paper cites F aith L ens: Detecting and Explaining Faithfulness Hallucination.

Decoupling Generation and Selection for Budget-Constrained Faithful Summarization F aith L ens: Detecting and Explaining Faithfulness Hallucination

Reference 59

Resolution: verified exact; doi, observed 2026-08-05T15:17:12.440843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers