Reranking-based Generation for Unbiased Perspective Summarization

Kathleen McKeown; Narutatsu Ri; Nicholas Deas

REVIEW 3 major objections 5 minor 27 references

Reranking-based Generation for Unbiased Perspective Summarization

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Reranking candidate summaries with LLM judges, then preference-tuning on those labels, produces more complete and more faithful political-perspective summaries than zero-shot inference or prompting frameworks.

desk verdict Worth a serious referee: the metric benchmark is a genuine resource and the reranking/DPO results are believable, though the independent human check is too small to settle the fine-grained ordering. read the letter →

arxiv 2506.15925 v1 pith:WLY5GPIM submitted 2025-06-19 cs.CL

Narutatsu Ri , Nicholas Deas , Kathleen McKeown This is my paper

classification cs.CL

keywords perspectivesummarizationcoveragefaithfulnessLLM-as-a-judgererankingdirectpreferenceoptimizationALIGNSCOREpoliticalbias

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that generating an unbiased summary of one political perspective is best done by sampling many candidate summaries and selecting with LLM-based judges, not by more elaborate prompting or attention tricks. It also claims that standard summary metrics—ROUGE and BERTScore—are not reliable for measuring perspective coverage and faithfulness, whereas an LLM judge for coverage and ALIGNSCORE for faithfulness are. To back this up, the paper builds a controlled test set with human-annotated key points and computes ground-truth coverage and faithfulness scores by construction. A sympathetic reader would care because it suggests a practical recipe—rerank first, then preference-tune on the reranker's own labels—that improves both completeness and faithfulness without needing human preference data.

What carries the argument

The central machinery is the reranking-as-labeling loop: an untrained backbone generates multiple candidate summaries, an LLM judge scores each for coverage and faithfulness, the top candidate is emitted, and in the DPO variant the pairwise score comparisons become synthetic preference labels for direct preference optimization. A second load-bearing component is the synthetic test set used to validate metrics: human annotators highlight key excerpts, an LLM paraphrases them into key points, adversarial key points are generated by reversing arguments or borrowing points from the opposing perspective, and ground-truth coverage and faithfulness are defined as ratios of key points included versus total, and included versus included-plus-unfaithful points.

What would settle it

Ask a fresh set of annotators to verify a random sample of the paraphrased and reversed key points against the source excerpts; if a substantial fraction of 'adversarial' key points are judged to no longer oppose the original argument, or paraphrases drop or add claims, the gold scores—and therefore the metric rankings and method ordering that depend on them—are not trustworthy.

Watch

Extended reading notes

Core claim

The paper establishes that, for multi-document political perspective summarization, traditional recall- and precision-based metrics fail: ROUGE and BERTScore correlate poorly with ground-truth faithfulness, while a prompted LLM judge (LLM-Coverage) reaches a Spearman correlation of 0.707 on coverage and ALIGNSCORE reaches a winrate of 0.773 on faithfulness. Using these validated metrics, the paper then shows that sampling nine candidate summaries with a Llama-3.1-8B-Instruct backbone and reranking them with Qwen2.5-14B-Instruct-based judges outperforms zero-shot inference, Multi-Agent Debate, Self-Refine, and the mechanistic attention method PINE on both automatic and human evaluation. Training the same backbone with Direct Preference Optimization on preference pairs produced by the reranker itself raises performance further—coverage and faithfulness improve by roughly 12% and 8% respectively under automatic scoring, and human evaluation confirms DPO+RR as the best method (0.437 coverage, 0.724 faithfulness).

Load-bearing premise

The paper's metric validation assumes that LLM-paraphrased highlights and LLM-reversed arguments preserve the identity and direction of the original article's key points, so if either transformation alters the meaning, the curated gold scores reward artifacts instead of real coverage and faithfulness.

Editorial extensions

If this is right

Reranking with LLM-based judges is a stronger inference-time strategy than prompting frameworks such as Multi-Agent Debate and Self-Refine for perspective summarization, and the gap persists even when prompting is scaled to nine agents or eighteen refinement rounds.
DPO training on synthetically generated, reranking-labeled preference pairs improves coverage and faithfulness over reranking alone, with the largest gains in faithfulness, offering a route to better faithfulness without human preference annotation.
Reranking-based methods are strongly abstractive: they increase novel n-gram ratios and faithfulness simultaneously, whereas PINE reduces abstractiveness and offers no quality gain.
Coverage and faithfulness are distinct qualities for perspective summaries: recall-style metrics do not measure faithfulness and precision-style metrics do not measure coverage, so evaluations should report both attributes separately.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the metric validation rests on LLM paraphrase and reversal preserving argument identity and direction, the reranking recipe's reported gains are also contingent on that assumption; a different judge family may rank candidates differently and change the magnitude of the DPO gains.
The same generate-score-preference-tune loop could be applied to other multi-document summarization tasks that have well-defined key points, such as product review summarization or neutral multi-news summarization; the paper does not test this.
An experiment comparing DPO+RR trained on reranking-generated labels against the same training on human preference labels would separate the contribution of the judge's scoring signal from the contribution of preference learning itself.
The paper's finding that LLM-Coverage correlates at 0.707 while LLM-Faithfulness trails ALIGNSCORE suggests that using separate judge types for the two attributes, rather than one holistic judge, is the better evaluation practice; this is consistent with the paper's own choice but is a generalizable principle.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Read the Ri/Deas/McKeown paper on reranking for perspective summarization. Short version: it's a solid, useful empirical paper, and the central claim holds up better than the stress-test note suggests, but the fine-grained boost from DPO is less certain than the abstract implies.

What's actually new: the first controlled test set for validating coverage and faithfulness metrics in multi-document political perspective summarization. Human annotators highlight excerpts, an LLM paraphrases them into key points and reverses some into adversarial points, and summaries are assembled from those pieces with known ground-truth counts. That's a clean way to benchmark metrics, and they do it carefully—correlation and winrate, several backbones, and the expected asymmetry where recall-based metrics fail at faithfulness and vice versa. The method comparison (zero-shot, self-refine, debate, PINE, reranking, DPO on reranked data) is also new in this setting, and the ablations that scale debate and self-refine are useful negative results.

What it does well: the paper is honest about its limitations. It discloses that the training data is synthetic, that the metric evaluation uses artificial error types, and that no novel metric is proposed. The human evaluation protocol is sensible, and IAA is reported against a random baseline. They kept a clean train/test split for DPO. The citation pattern is appropriate; POLI SUM is the obvious prior dataset, and they engage the relevant metric literature.

Soft spots. The stress-test worry about the synthetic test set is real but not fatal. If the LLM paraphrase or reversal changes meaning, the benchmark could reward metrics that track artifacts. That said, the human evaluation is an independent check, and it reproduces the same ranking across methods. So the central claims about reranking beating prompting and zero-shot do not rest solely on the synthetic set. The more serious limitation is the size of the human evaluation: 75 annotations per method, four annotators, no significance testing. The DPO+RR vs. Reranking gaps (0.437 vs. 0.410 coverage; 0.724 vs. 0.673 faithfulness) could easily be noise. The paper's own wording is careful in places, but the abstract's "significantly improves" is stronger than the human evidence supports. Minor point: DPO training labels use LLM-Faithfulness while automatic evaluation uses ALIGN SCORE, so training and evaluation objectives are not perfectly aligned.

Who it's for: anyone working on opinionated or perspective summarization, and anyone who wants an evidence-based answer on whether LLM-as-a-judge works for coverage and faithfulness. The metric test bed is a resource in itself.

Recommendation: send it to review. It deserves a serious referee. I'd ask for significance testing or a variance decomposition for the human eval, plus a small robustness check on a sample of paraphrases and reversals. Not a desk reject.

Referee Report

3 major / 5 minor

Summary. The paper addresses two gaps in political perspective summarization: the lack of validated automatic metrics for perspective coverage and faithfulness, and the under-exploration of generation methods beyond zero-shot inference. The authors construct a synthetic test set in which LLM-paraphrased human highlights define gold key points and LLM-reversed statements serve as adversarial key points, and they use it to benchmark ROUGE, BERTScore, BLEURT, SummaC, AlignScore, UniEval, MiniCheck, FineSurE, and two LLM-as-a-judge prompts (LLM-Coverage, LLM-Faithfulness). They find LLM-Coverage and AlignScore the most reliable for coverage and faithfulness respectively. They then compare zero-shot prompting, Self-Refine, Multi-Agent Debate, PINE, reranking with LLM-Coverage/LLM-Faithfulness, and DPO trained on reranking-labeled synthetic preferences, reporting that reranking is a strong baseline and that DPO+RR further improves coverage and especially faithfulness.

Significance. If the claims hold, the paper makes a useful contribution: it provides a benchmark for metric selection in perspective summarization, identifies reranking as a simple and strong inference-time baseline, and shows that synthetic preference data generated by reranking can further improve faithfulness via DPO. Strengths include the controlled test set built from human-highlighted excerpts, the inclusion of both automatic and human evaluation, an explicit inter-annotator agreement study, and public code. However, the central claims depend on the transferability of the synthetic test set to real generated summaries and on the statistical support for the DPO+RR versus reranking comparison; the current evidence leaves the headline faithfulness gain provisional.

major comments (3)

[§4 vs §3.2/Table 2 and §B.3] The reranking and DPO+RR methods are described as selecting and labeling summaries with LLM-Coverage and LLM-Faithfulness, yet Table 2 reports LLM-Faithfulness faithfulness winrate of 0.398±0.055, which is below the 0.500 random baseline stated in the caption, and §B.3 explicitly recommends avoiding prompt-based scoring for faithfulness because all backbones are near random in winrate. Using a proxy whose pairwise selection accuracy is at or below chance for the attribute being optimized is internally inconsistent, and it makes the faithfulness improvements of Reranking and DPO+RR difficult to interpret. The authors should either switch the faithfulness reranking/Dpo signal to AlignScore, or provide evidence that LLM-Faithfulness, despite its low pairwise winrate, is still useful for selecting among multiple candidates in the reranking setting.
[§3.1, Eqs. (1)-(2)] The gold coverage and faithfulness scores are computed by counting key points Kt,θ that are Qwen2.5-32B paraphrases of human-highlighted excerpts and adversarial key points K̄t,θ that are LLM-reversed statements. The validity of the benchmark requires that each paraphrase preserves the meaning and direction of the highlighted excerpt and that each reversed key point is genuinely unsupported by the source article; no human verification of these transformations is reported. Because Table 2's metric selection and §5.1's automatic evaluation both depend on this test set, any systematic paraphrase or reversal artifacts will be inherited by the metric rankings. A human validation sample of paraphrased and reversed key points, or a comparison of the benchmarked metrics against human scores on naturally generated summaries, is needed to establish that the synthetic test set transfers to real summarizer outputs.
[§5.2 and §4 (DPO training)] The headline result that DPO+RR 'further boosts' faithfulness over Reranking rests on a human evaluation with only 75 annotations per method, four annotators, and no significance testing; the reported faithfulness gap (0.724 vs 0.673) may be within noise. The IAA in Table 4 measures overlap of highlighted spans using a matching threshold τ rather than agreement on the final coverage/faithfulness scores, so it does not directly quantify the reliability of the human judgments used for the method comparison. In addition, §5.1's automatic evaluation is not an independent check because DPO+RR is trained on LLM-Coverage/LLM-Faithfulness labels and then evaluated with LLM-Coverage and AlignScore. The authors should report bootstrap or permutation confidence intervals for the human evaluation differences, provide per-annotator score distributions, and ideally collect additional annotations focused specifically on the Reranking vs DPO+RR comparison.

minor comments (5)

[Table 2] The LLM-Faithfulness row reports a faithfulness winrate of 0.398±0.055, which is below the stated random baseline of 0.500; the text should explicitly acknowledge this since the model is later used as a reranking proxy.
[Figure 3] The score panels have y-axes that do not start at zero; although the caption notes this, the visual differences between methods are exaggerated. Consider adding a broken-axis indicator or starting at zero.
[§4 (Preference Tuning with Reranking)] The DPO+RR description does not specify the number of preference pairs generated per input document or the DPO reference model used; adding these details would improve reproducibility.
[§3.1] The statement that the procedure 'produce[s] summaries with error levels ranging from few minor omissions to many faithfulness errors' would be easier to interpret with a histogram or table of the coverage/faithfulness score distribution in the curated test set.
[Table 1] The color-coded highlights (purple, blue, green, red, orange) may not be distinguishable in grayscale prints; consider using textual labels or symbols in addition to color.

Circularity Check

2 steps flagged · score 6.0 of 10

Automatic coverage comparisons for Reranking and DPO+RR are partially circular: candidates are selected and DPO labels are created with LLM-Coverage, which is also the §5.1 coverage evaluator; independent human evaluation and ALIGN-SCORE faithfulness keep the central claim partially supported.

fitted input called prediction [§4 'Reranking Generations'; §4.1 'Automatic Evaluation']
"we select the highest-scoring summary based on LLM-Coverage and LLM-Faithfulness. ... First, we assign numerical scores to summaries using LLM-Coverage and ALIGN SCORE (cf. §3)."

Reranking's selection objective is LLM-Coverage, and the automatic coverage evaluation in §5.1 is also LLM-Coverage. Reporting that Reranking improves coverage over zero-shot on this evaluator is therefore partly a selection artifact: the method is defined as choosing the candidate that maximizes the reported score. Using Qwen2.5-14B as the scorer and Mistral-7B as the evaluator reduces but does not remove the overlap, since the prompt and metric construct are the same.
fitted input called prediction [§4 'Preference Tuning with Reranking'; §4.1 'Automatic Evaluation']
"scoring them with LLM-Coverage and LLM-Faithfulness, and creating preference pairs by designating higher-scoring summaries as preferred over lower-scoring ones, which are then used to train the backbone."

DPO+RR is trained to prefer summaries that score higher on LLM-Coverage, and the paper's automatic coverage evaluation uses LLM-Coverage. The reported coverage gains for DPO+RR are thus partly the result of optimizing toward the test metric rather than an independent measurement. The faithfulness evaluation is less affected because it uses ALIGN SCORE, which is not the DPO label, and the human evaluation provides an independent check.

full rationale

The §3 metric-validation study is not circular: human-highlighted excerpts are paraphrased into key points, adversarial key points are generated by reversal, summaries are constructed from those points with ground-truth scores, and metrics are compared against these scores. The circularity is in the automatic method comparison. Reranking selects candidates by LLM-Coverage and LLM-Faithfulness, DPO+RR is trained on preference pairs labeled by those same scores, and §5.1 reports LLM-Coverage as the coverage evaluator. Hence the automatic coverage improvements are partly a selection/optimization artifact; the use of a different scorer backbone for reranking does not change the identity of the metric construct. The faithfulness result is more independent because ALIGN SCORE is the automatic faithfulness evaluator and is not used to label the DPO data, and the §5.2 human evaluation independently corroborates the method ordering. No load-bearing self-citation chain was found: POLI SUM is used as a task/dataset definition and related work is contextual. The synthetic-test-set transfer concern is a validity risk rather than circularity. Overall, partial circularity (6/10) because one reported prediction (automatic coverage gains) reduces to the selection/training objective, though independent human judgments and a separate faithfulness metric keep the central claim from being fully forced.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central empirical claims rest on LLM-generated key points and human annotations for ground truth, plus a specific numeric operationalization of coverage and faithfulness. No new theoretical entities are postulated. The free parameters are standard hyperparameters, but they influence the method comparison and should be flagged.

free parameters (5)

DPO beta = 0.1
Default hyperparameter from DPOConfig in the transformers library; fixed for all DPO+RR runs.
DPO learning rate = 5e-5 (adaptive)
Default from DPOConfig; controls the magnitude of preference updates, not tuned per task.
Number of reranking generations = 9
Number of candidate summaries generated per input before selection; chosen as the default setting, not tuned.
IAA matching threshold tau = 0.5
LCS overlap threshold for excerpt matching between annotators in the inter-annotator agreement measure.
Train/test split sizes = 1716/100
Split of POLISUM article pairs; the split determines the evaluation variance for method comparisons.

assumptions (5)

domain assumption LLM-paraphrased key points preserve the original excerpt's argument identity.
Used to construct the ground-truth test set in §3.1; if paraphrase changes meaning, gold scores are wrong.
domain assumption Human annotators reliably identify key points in articles and summaries.
Ground truth for metric evaluation and human eval of methods (§3.1, §4.1); only 5 annotators for the metric test set and 4 for summary eval, with IAA measured by string overlap.
domain assumption Definitions of perspective coverage and faithfulness as key-point ratios (Eqs. 1-2) are the correct operationalization of the intended qualities.
All benchmark and method scores are computed from these ratios; if the ratios miss important aspects of quality, the conclusions inherit that limitation.
domain assumption POLISUM dataset documents represent the two political perspectives faithfully.
The source dataset was created by Deas and McKeown (2025); the paper relies on its perspective labels for both training and evaluation.
standard math Standard model training and inference libraries (transformers, flash-attention) work as expected.
Background tooling assumption; implicit in all experiments.

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Cite this review

Pith. "Pith review of Reranking-based Generation for Unbiased Perspective Summarization." pith.science (2026). https://pith.science/paper/WLY5GPIM

@misc{pith2026250615925,
  author       = {Pith},
  title        = {Pith review of: Reranking-based Generation for Unbiased Perspective Summarization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WLY5GPIM}},
  note         = {Machine review of arXiv:2506.15925}
}

read the original abstract

Generating unbiased summaries in real-world settings such as political perspective summarization remains a crucial application of Large Language Models (LLMs). Yet, existing evaluation frameworks rely on traditional metrics for measuring key attributes such as coverage and faithfulness without verifying their applicability, and efforts to develop improved summarizers are still nascent. We address these gaps by (1) identifying reliable metrics for measuring perspective summary quality, and (2) investigating the efficacy of LLM-based methods beyond zero-shot inference. Namely, we build a test set for benchmarking metric reliability using human annotations and show that traditional metrics underperform compared to language model-based metrics, which prove to be strong evaluators. Using these metrics, we show that reranking-based methods yield strong results, and preference tuning with synthetically generated and reranking-labeled data further boosts performance. Our findings aim to contribute to the reliable evaluation and development of perspective summarization methods.

Figures

Figures reproduced from arXiv: 2506.15925 by the authors.

**Figure 2.** Example prompt for LLM-Coverage. We follow the prompt instruction format in Wu et al. (2024). Portions of the prompt are omitted for brevity. See §B.2 for complete prompt instructions. of unfaithful key points. We generate unfaithful key points by sampling key points from the opposing perspective (e.g., using key points from the left-leaning document for right-perspective summaries), and by reversing the content o… view at source ↗

**Figure 3.** Automatic (left) and human (right) evaluation results. For clarity, note that [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: Ablation study results. Figures 4a and 4b show that both prompting-based methods consistently underperform compared to reranking-based methods across all resource settings. Figure 4c shows that using a ROUGE-based proxy metric yields worse performance than LLM-based pr…

**Figure 5.** Figure 5: Prompt instruction for zero-shot inference [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]

**Figure 6.** Figure 6: Complete prompt instructions for both attributes in prompting-based scoring. The model is provided with [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

**Figure 7.** Figure 7: Prompts used for portions of the procedure for curating the benchmarking test set for metrics. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]

**Figure 8.** Figure 8: Coverage-density plots for source and opposing documents. PINE exhibits higher variance in coverage, [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]

**Figure 9.** Figure 9: Introduction page for Annotation interface for annotating for article excerpts to evaluate metrics. Annota [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

**Figure 10.** Figure 10: Example of annotation page for Annotation interface for annotating for article excerpts to evaluate [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]

**Figure 11.** Figure 11: Introduction page of annotation interface for annotating for document and summary excerpts for [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]

**Figure 12.** Figure 12: Example of annotation page for Annotation interface for document and summary excerpts for evaluating [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]

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Reference graph

Works this paper leans on

27 extracted references · 25 canonical work pages

[1]

No Coverage: The summary does not include any of the main arguments from the article

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Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization

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Perfect Coverage: The summary includes all major points and arguments mentioned in the article, leaving out nothing important. Follow these steps carefully:

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Low Coverage: The summary includes only a few of the main arguments from the article, omitting most

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Show all 27 references

[11]

High Coverage: The summary contains most of the main arguments from the article, missing only a few
[13]

Identify all major points, arguments, or facts it contains

**Read the Source Article**: Examine the text provided in the article. Identify all major points, arguments, or facts it contains
[14]

List each argument or point the summary includes

**Read the Summary**: Look at the text in the summary. List each argument or point the summary includes
[15]

- Count how many major points are covered versus how many are omitted

**Compare for Completeness**: - Check if each major point from the source article is present in the summary. - Count how many major points are covered versus how many are omitted
[16]

**Determine the Score**: - Assign a score from 1 (no coverage) to 5 (perfect coverage), based on how many main arguments are included in the summary relative to the source
[17]

--- # Source Article: (article) # Generated Summary: (summary) # Final Coverage Score (1~5 only): (a) Full prompt instructions for LLM-Coverage

**Output Instructions**: - Output only the final numeric score (1, 2, 3, 4, or 5) without any explanation or additional text. --- # Source Article: (article) # Generated Summary: (summary) # Final Coverage Score (1~5 only): (a) Full prompt instructions for LLM-Coverage. You ar...
[18]

Unfaithful: The summary is almost entirely incorrect or unrelated to the source
[19]

Mostly Unfaithful: The summary includes only a few relevant arguments or correct details but is largely incorrect or missing
[20]

Neutral: The summary has about half of the important points correct, but also includes notable inaccuracies or omissions
[21]

Mostly Faithful: The summary reflects most of the article 's arguments accurately, with only minor errors or omissions
[22]

Follow these steps carefully:

Perfectly Faithful: The summary includes all of the article 's main arguments accurately and does not introduce any contradictory or unsupported claims. Follow these steps carefully:
[23]

Identify the main points, arguments, or facts it contains

**Read the Source Article**: Examine the text provided in the article. Identify the main points, arguments, or facts it contains
[24]

Itemize or note each claim or statement made in the summary

**Read the Summary**: Look at the text in the summary. Itemize or note each claim or statement made in the summary
[25]

- Mark any claim that appears to be contradicting the source or not found in the source

**Compare for Accuracy**: - Check if each claim in the summary is explicitly or logically supported by the source. - Mark any claim that appears to be contradicting the source or not found in the source. - Check if the summary omits major arguments that are central to the source
[26]

**Determine the Score**: - Assign a score from 1 (completely unfaithful) to 5 (perfectly faithful), based on how many claims match (and do not contradict) the source article and whether key points are included
[27]

The article argues

**Output Instructions**: - Output only the final numeric score (1, 2, 3, 4, or 5) without any additional explanation or text. --- # Source Article: (article) # Generated Summary: (summary) # Final Faithfulness Score (1~5 only): (b) Full prompt instructions for LLM-Faithfulness...
[2017]

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In Thirty-seventh Conference on Neural Information Processing Sys- tems

Direct preference optimization: Your language model is secretly a reward model. In Thirty-seventh Conference on Neural Information Processing Sys- tems. Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown,...
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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

No Coverage: The summary does not include any of the main arguments from the article

work page

[2] [2]

Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization

The n+ implementation details of RLHF with PPO: A case study on TL;DR summarization. In First Conference on Language Modeling. Hayate Iso, Xiaolan Wang, Stefanos Angelidis, and Yoshihiko Suhara. 2022. Comparative opinion sum- marization via collaborative decoding. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3307–3324, Dub...

work page Pith review arXiv 2022

[3] [3]

Medium Coverage: The summary contains around half of the article 's main arguments

work page

[4] [4]

In Proceedings of the 61st Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6383–6402, Toronto, Canada

Parallel context windows for large language models. In Proceedings of the 61st Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6383–6402, Toronto, Canada. Association for Computational Linguistics. Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Ge- offrey Cideron, Robert Dadashi, Matthieu Geist, Ser- ta...

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[5] [5]

Follow these steps carefully:

Perfect Coverage: The summary includes all major points and arguments mentioned in the article, leaving out nothing important. Follow these steps carefully:

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[6] [6]

In Findings of the Associa- tion for Computational Linguistics: EMNLP 2023 , pages 2550–2575, Singapore

Large language models are better reasoners with self-verification. In Findings of the Associa- tion for Computational Linguistics: EMNLP 2023 , pages 2550–2575, Singapore. Association for Com- putational Linguistics. Yunshu Wu, Hayate Iso, Pouya Pezeshkpour, Nikita Bhutani, and Estevam Hruschka. 2024. Less is more for long document summary evaluation by L...

work page 2023

[7] [7]

coverage,

AlignScore: Evaluating factual consistency with a unified alignment function. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11328–11348, Toronto, Canada. Association for Computational Linguistics. Shiyue Zhang and Mohit Bansal. 2021. Finding a bal- anced degree of automation for s...

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Low Coverage: The summary includes only a few of the main arguments from the article, omitting most

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