SUMIE: A Synthetic Benchmark for Incremental Entity Summarization (2025.coling-main)
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| Challenge: | Existing datasets that test incrementally update entity summaries are lacking. |
| Approach: | They propose a fully synthetic dataset that exposes real-world IES challenges by generating diverse attributes, summaries, and unstructured paragraphs with 99% alignment accuracy. |
| Outcome: | The proposed dataset shows that state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. |
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Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang
| Challenge: | Existing models exhibit entity hallucination, generating names of entities that are not present in the source document. |
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EntSUMv2: Dataset, Models and Evaluation for More Abstractive Entity-Centric Summarization (2023.emnlp-main)
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| Challenge: | Entity-centric summarization is a form of controllable summarizing that aims to generate a summary for a specific entity given a document. |
| Approach: | They propose to use a more abstract version of the original entity-centric ENTSUM summarization dataset to generate a shorter annotated summary for downstream users. |
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Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference (P19-1)
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| Challenge: | Recent advances on abstractive summarization have led to fluent summaries, but factual errors in generated summary still severely limit their use in practice. |
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Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics (2021.naacl-main)
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| Challenge: | Modern summarization models generate fluent but often factually unreliable outputs. |
| Approach: | They propose to use human annotations to identify different categories of factual errors and benchmark factuality metrics to improve summarization evaluation. |
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From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models (2024.acl-long)
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| Challenge: | Prior work on timeline summarization has neglected the potential synergy between the two forms of timelines. |
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WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation (2021.acl-short)
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| Challenge: | Existing summarization datasets are limited in their ability to evaluate output . a human evaluation is necessary to understand and improve summarizing systems . |
| Approach: | They propose a dataset based on how-to articles and coherent paragraph summaries written in plain language. |
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Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling (2022.emnlp-main)
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| Challenge: | Abstractive summarization models often generate inconsistent summaries containing factual errors or fabricated content. |
| Approach: | They propose to generate representative examples of non-factual summaries through infilling language models and train a robust fact-correction model to post-edit them to improve factual consistency. |
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SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
| 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 . |
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EntSUM: A Data Set for Entity-Centric Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods for controllable summarization fail to generate entity-centric summaries. |
| Approach: | They propose to use a human-annotated data set EntSUM to generate controllable summarization with a focus on named entities as the aspects to control. |
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Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs (2024.emnlp-main)
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| Challenge: | Existing methods for extractive summarization lack coherence, despite improvements . a human-annotated dataset is used to improve coherency of extractive summary . |
| Approach: | They propose to use human-annotated datasets to create coherent extractive summaries . they use supervised fine-tuning and natural language user feedback to enhance coherence . |
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