Challenge: Using multilingual summarization evaluation methods is more reliable and interpretable than manual methods.
Approach: They propose to use multilingual BERT within BERTScore to evaluate summarization evaluation metrics . they use English datasets that are not representative of modern summarizing systems .
Outcome: The proposed methods perform well across all languages, at a level above that for English.

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Re-Evaluating Evaluation for Multilingual Summarization (2024.emnlp-main)

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Challenge: Existing studies have shown that automated evaluation approaches correlate with human ratings in English, but this is unclear for other languages.
Approach: They construct a small-scale pilot dataset containing article-summary pairs and human ratings in English, Chinese and Indonesian to measure the strength of summaries.
Outcome: The results show that standard metrics are unreliable measures of quality in Chinese and Indonesian.
Does Summary Evaluation Survive Translation to Other Languages? (2022.naacl-main)

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Challenge: a quality summarization dataset requires the production and evaluation of summaries by trained humans and machines.
Approach: They translate a summarization dataset in English and compare its performance to seven languages . they explore equivalence testing as an appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries .
Outcome: The proposed method could be used in seven languages and compares performance across measures.
A Survey on Cross-Lingual Summarization (2022.tacl-1)

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Challenge: Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language.
Approach: They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other .
Outcome: The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis.
Towards Multi-dimensional Evaluation of LLM Summarization across Domains and Languages (2025.acl-long)

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Challenge: Existing evaluation frameworks for text summarization lack domain-specific assessment criteria and are predominantly English-centric.
Approach: They propose a multi-dimensional, multi-domain evaluation of summarization in English and Chinese that incorporates specialized assessment criteria for each domain and leverages a debate system to enhance annotation quality.
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Multilingual Summarization with Factual Consistency Evaluation (2023.findings-acl)

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Challenge: Abstractive summarization models generate factually inconsistent summaries, reducing their utility for real-world applications.
Approach: They propose to use data filtering and controlled generation to detect hallucinations in machine generated summaries.
Outcome: The proposed models detect factual inconsistencies in machine generated summaries, but they focus on English only.
A Critical Look at Meta-evaluating Summarisation Evaluation Metrics (2024.findings-emnlp)

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Challenge: Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarization systems efficiently.
Approach: They argue that evaluation metrics are primarily meta-evaluated on news summarisation datasets and that there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries.
Outcome: The evaluation metrics are primarily meta-evaluated on news summarisation datasets and there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries.
Models and Datasets for Cross-Lingual Summarisation (2021.emnlp-main)

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Challenge: Recent years have witnessed increased interest in abstractive summarisation thanks to the popularity of neural network models and the availability of datasets containing hundreds of thousands of document-summary pairs.
Approach: They propose to create a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in . target language.
Outcome: The proposed task can be applied to several other languages and covers twelve languages and directions.
SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)

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Challenge: a lack of comprehensive studies on evaluation metrics for text summarization hinders progress . a new study aims to improve evaluation metrics that correlate with human judgments .
Approach: They propose to re-evaluate automatic evaluation metrics and share a toolkit for evaluation . they hope to promote a more complete evaluation protocol for text summarization .
Outcome: The proposed evaluation metrics are inconsistent with existing evaluation protocols.
Evaluating Factuality in Cross-lingual Summarization (2023.findings-acl)

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Challenge: Existing evaluation metrics for monolingual summarization require translation to evaluate the factuality of cross-lingual summmarization.
Approach: They propose to analyze cross-lingual factuality by collecting annotations and generated summaries from models at summary level and sentence level.
Outcome: The proposed dataset shows that over 50% of generated summaries contain factual errors with different characteristics from monolingual summarization.
How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation (2021.eacl-main)

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Challenge: Current manual evaluation methods for text summarization have low correlation with human judgements on summary quality.
Approach: They conduct two evaluation experiments on two aspects of summaries’ linguistic quality (coherence and repetitiveness) they find that study parameters such as the total number of annotators and distribution of annotes to annotation items are often not fully reported.
Outcome: The proposed methods can inflate type I errors up to eight-fold and the overall number of annotators can have a strong impact on study power.

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