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REVIEW 3 major objections 5 minor 62 references

Towards Multi-dimensional Evaluation of LLM Summarization across Domains and Languages

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper introduces MSumBench, a six-domain, English–Chinese summarization benchmark that uses domain-specific key facts and debate-assisted annotation, and reports a strong cross-task correlation plus measurable self-evaluation bias in…

desk verdict MSumBench is a useful, transparently built extension of the UniSumEval/FineSurE line, but the Chinese half rests on an unvalidated translation-equivalence assumption that anchors the headline cross-lingual comparisons. read the letter →

arxiv 2506.00549 v1 pith:UGFPTJDZ submitted 2025-05-31 cs.CL cs.AI

Hyangsuk Min , Yuho Lee , Minjeong Ban , Jiaqi Deng , Nicole Hee-Yeon Kim , Taewon Yun , Hang Su , Jason Cai
show 1 more author
Hwanjun Song
This is my paper
classification cs.CLcs.AI
keywords textsummarizationLLMevaluationmultilingualbenchmarkdomain-specifickeyfactsfaithfulnesscompletenessconcisenessself-evaluationbias
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

The paper builds MSumBench, a bilingual English–Chinese summary-evaluation benchmark spanning six domains, with key facts tailored to each domain and a multi-agent debate assistant that helps human annotators judge faithfulness. The authors claim that summarizers show distinct performance patterns by domain and language, that how well an LLM summarizes correlates with how well it evaluates summaries, and that LLMs judge their own summaries with systematic bias. This matters because automated evaluation of summarization is only as trustworthy as the labels it is tested against, and domain-blind, English-only labels can mislead conclusions about model quality.

What carries the argument

The backbone is a set of domain-specific key facts: atomic, one-clause information units drawn from each source document, grouped into roughly five to seven recurring categories per domain, extracted by an LLM and then filtered by three-LLM majority validation. Annotation is assisted by a three-agent debate system in which an Advocate argues for faithfulness, a Skeptic argues against it, and an Adjudicator checks both sides against the source before the human annotator decides; key-fact alignment is assisted separately by an NLI-style entailment check. Scores are percentage-based: faithfulness is the share of factually correct summary sentences, completeness is the share of key facts captured, and conciseness is the share of summary sentences that carry at least one key fact, with coefficient-of-variation based stability scores across domains and languages. This machinery is what converts summary quality from a holistic impression into domain-localized, sentence-level measurements.

What would settle it

Take a stratified sample of Chinese summaries and key facts, have native-Chinese annotators judge them directly from the original Chinese text, and compare those labels with the paper's English-translation labels; systematic disagreement on faithfulness or completeness would show the language comparison is contaminated by translation.

Watch

Extended reading notes

Core claim

The discovery is a benchmark construction plus a set of measured regularities: domain-structured evaluation surfaces patterns that uniform criteria miss. Across eight summarizers, proprietary LLMs lead on completeness and hold up better across languages, open-source LLMs degrade more sharply in Chinese, and no single LLM evaluator is best on every dimension. For LLM evaluators, agreement with human labels correlates at $\rho = 0.71$ with summarization performance, and self-evaluation bias appears in both directions, with some models over-rating their own summaries and at least one under-rating them. The authors also report that the distribution of factuality error types is similar across English and Chinese, which they interpret as faithfulness being largely a model-inherent property rather than a language-dependent one.

Load-bearing premise

Chinese summaries are translated into English before being annotated, so the Chinese quality scores assume the translation neither introduces nor removes factual content.

Editorial extensions

If this is right

  • Using one global evaluation rubric hides domain-dependent strengths, so future benchmarks should keep domain-specific key facts in mind.
  • Automated evaluator scores should be read per dimension, per domain, and per language, since no single LLM evaluator wins everywhere.
  • The positive cross-task correlation suggests that improving summarization ability and evaluation ability may reinforce each other.
  • Self-evaluation bias is not a single direction, so LLM judgments about their own summaries need model-specific correction rather than a one-size-fits-all adjustment.
  • Because factuality error distributions look similar across languages, fidelity improvements developed in one language may transfer to the other.

Reading between the lines

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

  • Editorial extension: since the Chinese summaries were graded after translation into English, a native-Chinese annotation sample would test whether the reported language differences come from the summaries or from the translation step.
  • The $\rho = 0.71$ correlation is associative; holding model family or parameter scale fixed and varying summarization training could test whether evaluation skill actually moves with it.
  • The domain-specific key-fact templates could plausibly be transplanted to other service-oriented summarization settings, such as medical records or meeting minutes, though the paper only demonstrates six domains.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper introduces MSumBench, a summarization benchmark covering six domains (news, medical literature, reports, booking, meetings, interviews) in English and Chinese, with domain-specific key-fact categories and a multi-agent debate-assisted annotation protocol. The authors construct 150 source documents (25 per domain), generate summaries from eight models, collect sentence-level faithfulness and key-fact alignment annotations, and use the resulting labels to benchmark both summarizers and LLM-based evaluators. The main empirical findings are that proprietary LLMs outperform open-source models especially in completeness, that performance varies across domains and languages, that a strong correlation (ρ=0.71) exists between LLM summarization and evaluation performance, and that self-evaluation bias appears in both directions.

Significance. If the validity concerns raised below are resolved, MSumBench would be a valuable community resource: it is one of the few benchmarks to combine multi-domain and bilingual coverage with domain-specific evaluation criteria, and the public release of the data and protocol is a strength. The expert validation of a 624-sentence subset of fact labels (balanced accuracy 92.83% versus 80.07% for UniSumEval) and the A/B preference results for domain-specific key-facts are concrete positive pieces of evidence. The multi-agent debate framework is a plausible mechanism for improving annotation accuracy, though its incremental benefit over simpler assistance methods is not isolated in the current design.

major comments (3)
  1. [3.1, Appendix H] Section 3.1 and Appendix H: Chinese summaries are annotated through English translations generated by GPT-4o, and the comparison source is the English document; the paper provides no evidence that this translation preserves faithfulness and completeness errors in the original Chinese summary. A hallucinated entity or a distorted relation in a Chinese summary could be corrected (or introduced) during translation, so the Chinese faithfulness and completeness scores reported in Table 5 and used in Figures 3–6 may not measure the generated Chinese summaries. Since the cross-lingual comparisons and the language-stability scores are central contributions, the equivalence assumption must be validated, for example by having bilingual annotators score the original Chinese summaries and comparing those scores to the translation-based labels.
  2. [3.4, Section 6] Section 3.4 and Section 6: The human labels used as ground truth for evaluating LLM-based summarizers and evaluators are produced with extensive LLM assistance: key-facts are generated by GPT-4o and validated by a three-LLM majority vote (Section 3.2), fact-verification annotations use Llama-3.1-70B debate agents with a tentative adjudicator label, and key-fact alignment is assisted by a Llama-3.1-70B NLI prediction. The expert spot-check in Section 4.2.2 covers 624 of 9,951 fact-verification sentence labels and does not cover the 188,800 key-fact alignment labels. Consequently, the agreement of LLM evaluators with these "human" labels, especially for Llama-3.1-70B, may be inflated by the model's own contribution to label generation. The authors should re-analyze the evaluator correlations on the expert-validated subset and report the impact of removing LLM assistance from the annotation protocol.
  3. [6.2, Figure 5] Section 6.2 and Figure 5: The reported cross-task correlation of ρ=0.71 is based on only eight data points (four models, two languages), and the two-tailed 0.05 significance threshold for Pearson's r with n=8 is about 0.707, making the result marginal rather than strong. The paper should report a non-parametric measure such as Spearman's rank correlation with a confidence interval, or otherwise account for the small sample size.
minor comments (5)
  1. [4.1, Table 3] The statement in Section 4.1 that the inter-annotator agreement of 0.58 (Krippendorff's alpha) for fact verification is "very high" is an overstatement; values around 0.6 are commonly considered moderate. The wording should be adjusted.
  2. [5.1, Appendix E] Language stability is computed as a coefficient of variation across only two languages, so the composite language-stability score rests on n=2 per dimension; this should be stated as a coarse indicator rather than a precise measure.
  3. [3.1, Tables 27–29] The use of only 25 documents per domain limits the precision of the per-domain scores in Tables 27–29; reporting confidence intervals or standard errors would help readers assess the significance of the observed model rankings.
  4. [4.2.2] The quality comparison between MSumBench and UniSumEval uses samples from two different datasets; without matched-difficulty data, the observed differences in balanced accuracy should be interpreted with caution.
  5. [5.2.2] The conclusion that "faithfulness in summarization is an inherent property of LLMs" is too strong given that the Chinese error distributions come from translated summaries and the language comparison may be affected by translation artifacts.

Circularity Check

2 steps flagged · score 6.0 of 10

The benchmark's reference key-facts are generated and validated by the same LLMs that are scored, and the 'human' labels used to benchmark Llama-3.1-70B as an evaluator are produced with Llama-3.1-70B's own tentative labels, so key results are partly self-referential.

  1. fitted input called prediction [Section 3.2 (Key-Fact Extraction) with Appendix E.1 (Completeness formula) and Table 5]
    "we use GPT-4o to generate candidate key-facts from source documents ... each filtered key-fact is verified by three LLMs—GPT-4o, Claude-3.5-sonnet, and Llama-3.1-70B. ... The resulting consensus-based key-facts constitute a robust domain-specific reference set, forming the backbone of our subsequent evaluation of summary quality. ... Completeness(K, S) = |{k|(k, s)∈E}| / |K|"

    The completeness and conciseness dimensions are computed against the key-fact set K, but K was generated by GPT-4o and majority-validated by GPT-4o, Claude-3.5-Sonnet, and Llama-3.1-70B—the very models whose summarization scores are reported in Table 5. For GPT-4o, its 'completeness' score therefore measures, by construction, how well a GPT-4o summary covers key-facts that GPT-4o itself chose; an analogous self-agreement holds for the other validator models. The expert A/B test in Section 4.2.1 compares domain-specific with generic key-facts but does not establish that K is independent of the summarizers under test, so the headline finding of superior domain-characteristic capture is partly a comparison of each model with its own notion of important content.

  2. self definitional [Section 3.4 (fact verification and key-fact alignment), Section 6 (LLMs as evaluators), Table 6]
    "Finally, the Adjudicator produces an investigation report and a tentative label, which are shown to the annotator along with the reference text and both sides’ arguments. We use Llama-3.1-70B for all three agents. ... we present annotators with an NLI result generated by Llama-3.1-70B ... We compare two proprietary LLMs (Claude-3.5-Sonnet, GPT-4o) and two open-source LLMs (Qwen-2.5-72B and Llama-3.1-70B)"

    The gold-standard labels used in Section 6 are not independent of the evaluator being scored. For faithfulness, the Llama-3.1-70B Adjudicator's tentative label is shown to annotators; for completeness/conciseness, a Llama-3.1-70B NLI result is shown. The paper then measures Llama-3.1-70B's correlation with these 'human' labels (Table 6). That correlation is therefore, to an unknown degree, agreement of Llama-3.1-70B with its own previously generated suggestions, not agreement with an independent human judgment. Annotators could override the suggestions, so the correlation is not exactly 1, but the reported accuracy is inflated by construction and cannot be read as external validation of Llama-3.1-70B as an evaluator.

full rationale

The central derivations are partially self-referential. The completeness reference (key-fact set) is produced by the same models whose summarization performance it is used to score, and the faithfulness/completeness 'human' labels are produced with Llama-3.1-70B assistance before Llama-3.1-70B is benchmarked as an evaluator against those labels. These are the two load-bearing reductions; I quote the exact passages above. The paper does include independent elements: two expert examiners re-annotate a 624-sentence sample for fact verification in Section 4.2.2, and expert A/B preference testing supports the domain-specific key-fact design. Those checks prevent a score of 8 or higher, but they do not remove the self-comparison from the main benchmark tables, and they do not validate completeness gold standards for independence. Separately, Appendix H states that Chinese summaries are translated into English with GPT-4o before MTurk annotation; this is a validity threat to the Chinese-language scores because factuality preservation through translation is not demonstrated, but it is an assumption about measurement validity rather than a derivation that reduces to its own inputs, so I do not count it as a circular step. Overall, the paper's headline rankings and evaluator-correlation claims are partly forced by the benchmark's construction, yielding a partial circularity score of 6.

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

The benchmark depends heavily on LLM-generated content at several stages: key-facts are produced and validated by GPT-4o, Claude-3.5-Sonnet, and Llama-3.1-70B; fact annotation is assisted by Llama-3.1-70B debate; Chinese annotation operates on GPT-4o translations. These are unproven assumptions about the validity and completeness of the reference content and labels, and they make the human ground truth partially machine-generated.

free parameters (5)
  • Documents per domain = 25
    25 documents sampled per domain (150 total); no power analysis, so cross-domain comparisons have unknown uncertainty.
  • Expert-checked fact-verification sample = 624 sentences
    Only 624 of 9,951 fact-verification sentences were expert-reannotated, limiting independent validation of the full label set.
  • Key-fact category count per domain = 5-7
    Taxonomy size is a modeling choice derived from reference summaries and affects all completeness and conciseness scores.
  • Summary generation temperature = 1.0
    Fixed at 1.0 in Appendix B.3; scores may vary with sampling, but no repeated generation is reported.
  • Annotators per annotation unit = 3
    Three MTurk workers per unit were used to compute IAA; majority or consensus handling is not described.
assumptions (5)
  • domain assumption LLM-assisted human annotation is accurate enough to serve as ground truth.
    Sec 3.4: annotators receive advocate/skeptic/adjudicator arguments and NLI hints; all later measurements assume these labels are correct.
  • domain assumption English translation preserves the faithfulness-relevant content of Chinese summaries.
    Appendix H: Chinese summaries are translated with GPT-4o and then annotated in English; any translation loss or repair changes the measured errors.
  • domain assumption Translated Chinese source documents are valid substitutes for native Chinese documents.
    Sec 3.1: original English documents are translated to Chinese; model inputs are therefore translationese, not naturally occurring Chinese.
  • ad hoc to paper Three-LLM majority vote validates key-fact quality.
    Sec 3.2: GPT-4o, Claude-3.5-Sonnet, and Llama-3.1-70B vote to accept key-facts; no human validation is performed for the full key-fact set.
  • domain assumption IAA thresholds used to declare annotations reliable.
    Sec 4.1: alpha=0.58 for fact verification is interpreted as very high; no external benchmark or justification is given for this threshold.

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Cite this review

Pith. "Pith review of Towards Multi-dimensional Evaluation of LLM Summarization across Domains and Languages." pith.science (2026). https://pith.science/paper/UGFPTJDZ

@misc{pith2026250600549,
  author       = {Pith},
  title        = {Pith review of: Towards Multi-dimensional Evaluation of LLM Summarization across Domains and Languages},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UGFPTJDZ}},
  note         = {Machine review of arXiv:2506.00549}
}
read the original abstract

Evaluation frameworks for text summarization have evolved in terms of both domain coverage and metrics. However, existing benchmarks still lack domain-specific assessment criteria, remain predominantly English-centric, and face challenges with human annotation due to the complexity of reasoning. To address these, we introduce MSumBench, which provides a multi-dimensional, multi-domain evaluation of summarization in English and Chinese. It also incorporates specialized assessment criteria for each domain and leverages a multi-agent debate system to enhance annotation quality. By evaluating eight modern summarization models, we discover distinct performance patterns across domains and languages. We further examine large language models as summary evaluators, analyzing the correlation between their evaluation and summarization capabilities, and uncovering systematic bias in their assessment of self-generated summaries. Our benchmark dataset is publicly available at https://github.com/DISL-Lab/MSumBench.

Figures

Figures reproduced from arXiv: 2506.00549 by the authors.

Figure 1
Figure 1. Overview of MSumBench, featuring multi-domain documents in both English and Chinese, with domain￾specific key-facts. Model summaries are evaluated via a multi-agent debate framework, aiding annotators’ assess￾ments. Each summary then receives percentage scores for faithfulness, completeness, and conciseness. marization annotation task. Our debate-based sys￾tem provides annotators with structured arguments from LLM a… view at source ↗
Figure 2
Figure 2. Annotation assistance via multi-agent debate. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Key-fact category coverage ratio in two domains for English and Chinese. Abbreviations on the x-axis [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Distribution of error types across summarizers [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 6
Figure 6. Figure 6: Self-evaluation bias rates across models and [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Reference graph

Works this paper leans on

62 extracted references · 58 canonical work pages

  1. [1]

    Reference Consistency • Keep organization names in original form • Translate ALL PERSON NAMES to Chinese following appropriate conventions: –Western names: Use standard Chinese transliteration * Example: Michael→迈克尔(Màikè’ ˇer), John→约翰(Yu ¯ehàn) –Chinese names: Maintain Chinese characters * Keep family name and given name format (e.g.,王小明) –For establish...

    work page

  2. [4]

    translation

    Chinese Writing Style Consistency • Use formal written Chinese (书面语) consistently • Avoid mixing formal and colloquial expressions • Follow standard news writing conventions: –Use proper判断词and状态词 –Use standard news article punctuation • Word choice guidelines: –Prefer因为over由于for causation –Use表示instead of说for formal statements –Choose认为over觉得for opinions ...

    work page

  3. [5]

    LongEval: Guidelines for human evaluation of faithfulness in long-form summarization. InEACL. Philippe Laban, Wojciech Kry´sci´nski, Divyansh Agar- wal, Alexander Richard Fabbri, Caiming Xiong, Shafiq Joty, and Chien-Sheng Wu. 2023. SummEdits: Measuring llm ability at factual reasoning through the lens of summarization. InEMNLP. Philippe Laban, Tobias Sch...

    arXiv 2023

  4. [6]

    Multilingual fine-grained news headline hallu- cination detection. InEMNLP. Hwanjun Song, Hang Su, Igor Shalyminov, Jason Cai, and Saab Mansour. 2024. FineSurE: Fine-grained summarization evaluation using llms. InACL. Liyan Tang, Tanya Goyal, Alex Fabbri, Philippe Laban, Jiacheng Xu, Semih Yavuz, Wojciech Kry ´sci´nski, Justin Rousseau, and Greg Durrett. ...

    arXiv 2024

  5. [7]

    Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel

    Self-preference bias in llm-as-a-judge. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2020. mt5: A massively multilingual pre-trained text-to-text transformer. InarXiv preprint arXiv:2010.11934. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayihe...

    arXiv 2020

  6. [8]

    </note> <instructions>

    Even if the summary sentence draws reasonable implications, logical conclusions, or appropriate generalizations, it remains factually consistent with the reference document as long as these are explicitly supported by the original meaning. </note> <instructions>

    work page

  7. [9]

    Alignscore: Evaluating factual consistency with a unified alignment function. InACL. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Wein- berger, and Yoav Artzi. 2019. BERTScore: Evaluat- ing text generation with bert. InICLR. Chenguang Zhu, Yang Liu, Jie Mei, and Michael Zeng

    work page 2019

  8. [16]

    Medical Terminology • Use standardized Chinese medical terms (规范医学用语) • Keep precision in medical concepts: –Diseases: Standard Chinese names (英文名) –Medications: Generic names in Chinese (英文通用名) –Medical procedures: Standard translations • Handle technical terms: –First mention: Chinese term (English term); Following mentions: Chinese term only • Maintain...

    work page

Show all 62 references
  1. [17]

    translation

    Academic Writing Style • Use formal academic Chinese (学术用语) • Follow scientific writing conventions: use precise and objective language and maintain the scientific tone • Sentence structure: Clear and concise, logical flow, one key point per sentence • Word choice guidelines: ...

  2. [18]

    Reference Consistency • Keep organization names in original form • Translate ALL PERSON NAMES to Chinese following appropriate conventions: –Western names: Use standard Chinese transliteration (Michael→迈克尔(Màikè’ ˇer)) –Chinese names: Maintain Chinese characters • For establis...

  3. [19]

    Technical Terms • Use established Chinese technical terms • First mention: Chinese term (English term); Following mentions: Chinese term only • Maintain consistency in specialized terms throughout

  4. [20]

    Cultural Adaptation • Translate English idioms and proverbs to Chinese cultural equivalents (成语when appropriate) • Convert Western business expressions to match Chinese business etiquette: –Use appropriate level of formality (敬语) –Follow Chinese business conversation conventio...

  5. [21]

    translation

    Chinese Writing Style Consistency • Use the highest level formal written Chinese (政府公文体) • Follow official document writing conventions: –Use standard official vocabulary (规范用语) –Apply proper ceremonial words (礼仪用语) • Use formal written Chinese (书面语) consistently • Avoid mixin...

  6. [22]

    Reference Consistency • Keep organization names in original form • Translate ALL personal names to Chinese following appropriate conventions: –Western names: Use Chinese transliteration (James→詹姆斯) –Chinese names: Keep Chinese characters – For established figures, use their co...

  7. [23]

    Technical Terms • Use established Chinese technical terms • First mention: Chinese term (English term); Following mentions: Chinese term only

  8. [24]

    Speaker: dialogue

    Speaking Style & Format • Keep "Speaker: dialogue" format –Place actions in parentheses –Maintain conversation flow • Use appropriate formal Chinese based on context • Keep each speaker’s tone consistent • se proper conversational particles (吧，呢，啊) • Adapt greetings and courte...

  9. [25]

    Michael"→

    Reference Consistency • Keep organization names in original form • Translate ALL PERSON NAMES to Chinese following appropriate conventions: –Western names: Use standard Chinese transliteration * Example: "Michael"→"迈克尔" (Màikè’ ˇer), "John"→"约翰" (Yu ¯ehàn) –Chinese names: Main...

  10. [26]

    • First mention: Chinese term (English term); Following mentions: Chinese term only • Maintain consistency in specialized terms throughout

    Technical Terms • Use established Chinese technical terms • Follow terms already defined in the document for consistency. • First mention: Chinese term (English term); Following mentions: Chinese term only • Maintain consistency in specialized terms throughout

  11. [27]

    • Avoid any stylistic adjustments or embellishments

    Focus on Information • Prioritize the accurate transfer of factual information in each key fact. • Avoid any stylistic adjustments or embellishments. Translate the text plainly and faithfully

  12. [28]

    translation

    Back-Translation for Verification: • For each translated Chinese sentence: –Perform a back-translation into English. –Compare the back-translation with the original English key fact. • If there is any difference in meaning, revise the Chinese translation and repeat Steps 1–3 u...

  13. [29]

    standard Simplified Chinese characters

    We present additional domain-level findings. Faithfulness ScoreTable 27 shows propri- etary LLMs outperform open-source and non- LLMs across domains and languages. Specifically, Claude-3.5-Sonnet achieves optimal performance stability among proprietary LLMs across all do- main...

    2022

  14. [30]

    Accuracy • Compare the English source text and Chinese translation to ensure meaning is preserved • Check for any omissions or additions • Verify numerical values, dates, and proper names are correctly translated • Flag any mistranslations or semantic errors

  15. [31]

    Consistency • Reference Consistency: Check if proper nouns, organization names, and product names are translated consistently • Technical Term Consistency: Verify industry-specific terminology is translated consistently and correctly • Style Consistency: Ensure consistent tone...

  16. [32]

    Fluency • Check if the translation reads naturally in Chinese • Verify proper Chinese grammar and syntax • Ensure appropriate sentence structure and flow • Check for any awkward expressions or unnatural phrasing

  17. [33]

    Readability • Assess if the text is easy to understand for the target audience • Check sentence length and complexity • Verify proper paragraph breaks and text organization • Ensure clear logical flow

  18. [34]

    Cultural Appropriateness • Check for cultural sensitivity • Verify idioms and expressions are appropriately localized • Ensure measurements, dates, and currencies are properly converted • Flag any potential cultural misunderstandings

  19. [35]

    Professionalism • Verify appropriate formal/business language usage • Check for proper honorific forms • Ensure professional terminology is correctly used • Maintain appropriate level of formality

  20. [36]

    translation

    Fitness for Purpose • Verify the translation meets its intended purpose • Check if appropriate for target audience • Ensure industry-specific requirements are met • Verify technical accuracy for specialized content Provide the answer using the following JSON format: {"translat...

  21. [37]

    Each key-fact should: • Be a complete sentence with a subject, verb, and object/complement

    Identify Key-facts: Extract all key-facts from the text. Each key-fact should: • Be a complete sentence with a subject, verb, and object/complement. • Contain only one action, event, or idea. Avoid compound sentences. • Include no more than two or three entities per key fact. ...

  22. [38]

    • Key-facts (Revised): (a) The resolution authorizes the operation of one winter shelter

    Here are the examples of key-fact structure granularity: • Text Example: The resolution authorizes the operation of one winter shelter from December 1st, 2019, to March 31st, 2020. • Key-facts (Revised): (a) The resolution authorizes the operation of one winter shelter. (b) Th...

    2019

  23. [39]

    – Assign each key-fact to a category

    Categorize Key-facts: – Define your own categories for the key-facts based on content. – Assign each key-fact to a category

  24. [40]

    – Adjust any key-fact to better align with the provided categories

    Compare Categories: – Compare your defined categories with the provided key-fact categories. – Adjust any key-fact to better align with the provided categories

  25. [41]

    key_facts

    Validate Key-facts • Ensure each key-fact meets the following criteria: –Correctly categorized. –Atomicity: Conveys only one action, event, or idea. –Clarity: Is concise and clear, avoiding ambiguity. –Brevity: Contains no unnecessary details. –Non-overlapping: Does not duplic...

  26. [42]

    Read a Document and a set of Key-facts sentences carefully

  27. [43]

    Evaluate each Key-facts sentence based on the five reasons above

  28. [44]

    Yes", otherwise response

    According to evaluation, if the Key-facts sentence is useful for making a summary of the Source Document, response "Yes", otherwise response "No"

  29. [45]

    sentence

    Provide a single sentence explaining why the Key-facts sentence is useful for making a summary. Provide your answer in JSON format. The answer should be a list of dictionaries whose keys are "sentence", "response", "reason". you should provide a response and a reason for all K...

  30. [46]

    The complete information conveyed by the sentence in List A is entirely present in the sentence B

  31. [47]

    The essential meaning of the sentence in List A must align with or be fully and explicitly captured by sentence B

  32. [48]

    sentence A

    The information conveyed by the sentence in List A must be explicitly implied or fully understood by sentence B. • Exact wording is not required, but the complete and explicit meaning must match. • Provide a short reason, and a label: contained, not contained. Please provide y...

  33. [49]

    – Compare each summary sentence to the reference document to identify evidence supporting its factual consistency

    Read the reference document and summary sentence under your role: – Carefully read the reference document and try to fully understand it. – Compare each summary sentence to the reference document to identify evidence supporting its factual consistency. – Refer to the error typ...

  34. [50]

    – Even if a perfect match cannot be found, select the closest sentence(s) that contain key elements (entities, relationships, events, quantities, or cause-effect relationships)

    As a ADVOCATE, focus on finding alignment: – Explicitly identify numbered sentences in the reference document that support or partially align with the summary sentence. – Even if a perfect match cannot be found, select the closest sentence(s) that contain key elements (entitie...

  35. [51]

    reference_sentence_number

    Provide a detailed explanation of your arguments: – For each summary sentence: •Cite one or more numbered sentences from the reference document, even if only partial alignment exists. •Use the format "reference_sentence_number": [number1, number2] to explicitly indicate the re...

  36. [52]

    – Compare each summary sentence to the reference document to identify evidence supporting its factual consistency

    Read the reference document and summary sentence under your role: – Carefully read the reference document and try to fully understand it. – Compare each summary sentence to the reference document to identify evidence supporting its factual consistency. – Refer to the list of e...

  37. [53]

    – Even if a perfect match cannot be found, select the closest sentence(s) that contain key elements (entities, relationships, events, quantities, or cause-effect relationships)

    As a SKEPTIC, focus on identifying discrepancies: – Explicitly identify numbered sentences in the source document that contradict or fail to align with the summary sentence. – Even if a perfect match cannot be found, select the closest sentence(s) that contain key elements (en...

  38. [54]

    reference_sentence_number

    Provide a detailed explanation of your arguments: – For each summary sentence: •Cite one or more numbered sentences from the reference document, even if only partial alignment exists. •Use the format "reference_sentence_number": [number1, number2] to explicitly indicate the re...

  39. [55]

    Faithfulness measures how accurately a summary sentence reflects the source document’s information and content

  40. [56]

    The summary sentence should not have to use exact wording in the reference document as long as the original meaning is preserved

  41. [57]

    The summary sentence can paraphrase and use alternative expressions with preserving the original meaning

  42. [59]

    The summary sentence is factually consistent even if it omits specific details-one, some or all from reference document

  43. [60]

    The summary sentence is factually consistent even if it modifies the level of specificity (using broader terms instead of detail and specific information, or more specific terms instead of the broader terms), maintaining the original information

  44. [61]

    The summary sentence is factually consistent even if it combines multiple pieces of information from different parts of the text maintaining the original meaning without contradiction

  45. [62]

    – Develop a comprehensive understanding of both the reference document and summary sentence and how it’s been summarized

    Read the reference document and summary: – Carefully review the reference document and the summary sentence provided. – Develop a comprehensive understanding of both the reference document and summary sentence and how it’s been summarized

  46. [63]

    – Validate the claims align with the reference document and avoid unsupported speculation

    Evaluate the validity of agent arguments: – Compare both agents’ reasoning critically. – Validate the claims align with the reference document and avoid unsupported speculation. – Ensure the claims follow the guidelines in <note></note> tags

  47. [64]

    Finalize your own judgment of the summary sentences. – Make a final decision on whether the summary sentence is factually consistent with the reference document, based on your understanding of reference document, summary sentence and the validation of the two opposing claims

  48. [65]

    summary_sentence_num

    Provide your final decision as error type and label – Assign an error type and label as follows: – error_type: refer to the error_type listed below: •no error: no error found, and the summary is factually consistent with the reference document. • out-of-article error: If the s...

  49. [2020]

    InNLP4ConvAI

    MultiWOZ 2.2 : A dialogue dataset with addi- tional annotation corrections and state tracking base- lines. InNLP4ConvAI. Yuheng Zha, Yichi Yang, Ruichen Li, and Zhiting Hu

  50. [2021]

    Mediasum: A large-scale media interview dataset for dialogue summarization. InNAACL. Dataset Domain English Chinse Text Word count (Min - Max) Summary Word count (Min - Max) Text Word count (Min - Max) Summary Word count (Min - Max) Key-fact Count (Min - Max) CNN/DM News 503.6...

    2024

  51. [2022]

    InProceedings of the ACM Web Conference 2022, pages 1720–1730

    Measuring annotator agreement generally across complex structured, multi-object, and free-text annotation tasks. InProceedings of the ACM Web Conference 2022, pages 1720–1730. Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu

    2022

  52. [2023]

    Meetingbank: A benchmark dataset for meet- ing summarization. InACL. Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, and Lu Wang. 2021. Efficient attentions for long document summarization. InACL. Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh ...

    2021

  53. [2024]

    Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian

    Chateval: Towards better llm-based evaluators through multi-agent debate. Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. In NAACL...

    2018

  54. [2025]

    arXiv preprint arXiv:2502.08514

    Faithful, unfaithful or ambiguous? multi-agent debate with initial stance for summary evaluation. arXiv preprint arXiv:2502.08514. Klaus Krippendorff. 2011. Computing krippendorff’s alpha-reliability. Kalpesh Krishna, Erin Bransom, Bailey Kuehl, Mohit Iyyer, Pradeep Dasigi, Ar...

    2011 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.