TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models (2023.emnlp-main)
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| Challenge: | Existing methods for evaluating factual consistency are limited in their effectiveness. |
| Approach: | They propose a method for generating synthetic data by annotating diverse model-generated summaries using a LLM. |
| Outcome: | The proposed method outperforms state-of-the-art models and the LLM teacher on TRUE benchmarks. |
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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. |
Evaluating the Factual Consistency of Large Language Models Through News Summarization (2023.findings-acl)
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| Challenge: | Existing LLMs generally assign a higher score to factually consistent summaries than to factualally inconsistent summary. |
| Approach: | They propose a benchmark to measure whether large language models prefer factually consistent continuations of inputs. |
| Outcome: | The proposed benchmark compares the scores an LLM assigns to a factually consistent versus a inconsistent summary for an input news article. |
A Tale of Evaluating Factual Consistency: Case Study on Long Document Summarization Evaluation (2025.findings-acl)
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| Challenge: | Despite the recent progress for summarization models in producing fluent summaries, they still encounter challenges when long sequences of generated texts and inputs (over thousands of words) need to be evaluated. |
| Approach: | They conduct a systematic analysis of factual-consistency evaluation systems across four long-document datasets and examine the relationship between sentence-level and summary-level model performance. |
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Evaluating the Factual Consistency of Abstractive Text Summarization (2020.emnlp-main)
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| Challenge: | a weakly-supervised approach is needed to verify factual consistency . auxiliary span extraction tasks are useful for verifying factual consistent summaries . |
| Approach: | They propose a weakly-supervised approach for verifying factual consistency . they transfer the model to summaries generated by several neural models . |
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Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency (2022.emnlp-main)
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| Challenge: | Abstractive summarization systems have a lack of a defined definition for the task . factual consistency is a key factor in summarizing, but there are still deficiencies . a new study shows that summarized summarisation models achieve improved performance . |
| Approach: | They propose a filtered summarization dataset with improved factual consistency to address this problem . they argue that the dataset should become a valid benchmark for developing and evaluating summarizing systems . |
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Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization (2022.naacl-main)
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| Challenge: | Neural abstractive summarization models generate factually inconsistent summaries . previous work has introduced the task of recognizing factual inconsistency as a downstream application of natural language inference (NLI). |
| Approach: | They propose a data generation pipeline that enables a task-oriented approach to detect factual inconsistencies in abstractive summarization models. |
| Outcome: | The proposed model improves the state-of-the-art performance across four benchmarks for recognizing factual inconsistency in generated summaries. |
SummaCoz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization (2024.findings-emnlp)
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| Challenge: | Summarization is an important application of Large Language Models. |
| Approach: | They integrate human-annotated and model-generated natural language explanations to elucidate how a summary deviates and becomes inconsistent with its source article. |
| Outcome: | The proposed model provides rationales for its judgments and improves its accuracy significantly. |
Factual Dialogue Summarization via Learning from Large Language Models (2025.coling-main)
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| Challenge: | Existing models generate fluent and coherent summaries, but inconsistencies can be found in generated summary. |
| Approach: | They propose to use symbolic knowledge distillation to improve the factual consistency of smaller pretrained models for dialogue summarization. |
| Outcome: | The proposed model outperforms baseline models in BART, PEGASUS, and Flan-T5 in factual consistency and accuracy. |
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking (2022.findings-naacl)
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| Challenge: | Abstractive summarization systems generate paraphrases, but they often contain information inconsistent with the source text. |
| Approach: | They propose to generate factually inconsistent summaries using source texts and reference summary with key information masked to train a factual consistency classifier. |
| Outcome: | The proposed method outperforms existing models and shows a competitive correlation with human judgments. |
Factual Consistency of Multilingual Pretrained Language Models (2022.findings-acl)
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| Challenge: | Recent work shows that monolingual English language models fill-in-the-blank differently for paraphrases describing the same fact. |
| Approach: | They propose a resource to analyze consistency of English language models . they find that mBERT is as inconsistent as English BERT in paraphrases . |
| Outcome: | The proposed model is as inconsistent as English BERT in English paraphrases, but it is more so for all the other 45 languages. |