| Challenge: | SNaC framework is used to evaluate long summaries, but it fails to identify gaps in coherence . nallapati and colleagues have developed a framework for fine-grained annotations of long summarizations . |
| Approach: | They propose a narrative coherence evaluation framework for fine-grained annotations of long summaries that can be used to evaluate coherent narratives. |
| Outcome: | The proposed framework can support future work in document summarization and coherence evaluation, the authors show . |
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| Challenge: | Existing methods to evaluate summary coherence are often evaluated using disparate datasets and metrics. |
| Approach: | They propose to use automatic evaluation to evaluate coherence of summaries by selecting high-scoring candidates. |
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| Challenge: | Recent abstractive summarization systems produce factual errors that are not faithful to the input . current methods are lacking in identifying what errors are most important to target . |
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Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors (2023.acl-long)
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Liyan Tang, Tanya Goyal, Alex Fabbri, Philippe Laban, Jiacheng Xu, Semih Yavuz, Wojciech Kryscinski, Justin Rousseau, Greg Durrett
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Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
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| Challenge: | Abstractive summarization has made tremendous progress in recent years . however, even under a short document setting, abstractive models often generate summaries that are repetitive, ungrammatical, and factually inconsistent with the source. |
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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. |
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FALTE: A Toolkit for Fine-grained Annotation for Long Text Evaluation (2022.emnlp-demos)
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| Challenge: | Existing tools to evaluate long text outputs are lacking in the field of NLP . human rating and error analysis remains a crucial component for any evaluation of long text generation. |
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How coherent are neural models of coherence? (2020.coling-main)
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| Challenge: | Existing approaches to model coherence are limited to small newswire corpora . evaluators need to be trained on lexical and document levels to perform evaluations . |
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STORYSUMM: Evaluating Faithfulness in Story Summarization (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating abstractive summarization are lacking in faithfulness evaluation. |
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COHESENTIA: A Novel Benchmark of Incremental versus Holistic Assessment of Coherence in Generated Texts (2023.emnlp-main)
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| Challenge: | linguistics has been used to assess the coherence of generated texts . a benchmark of coherency scores is developed to measure the quality of generated text . |
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