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Cross-Document Event-Keyed Summarization

arXiv 2410.14795 ↗First posted 18 Oct 2024cs.CLcited by 0

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Abstract (arXiv)

Event-keyed summarization (EKS) requires summarizing a specific event described in a document given the document text and an event representation extracted from it. In this work, we extend EKS to the cross-document setting (CDEKS), in which summaries must synthesize information from accounts of the same event as given by multiple sources. We introduce SEAMUS (Summaries of Events Across Multiple Sources), a high-quality dataset for CDEKS based on an expert reannotation of the FAMUS dataset for cross-document argument extraction. We present a suite of baselines on SEAMUS -- covering both smaller, fine-tuned models, as well as zero- and few-shot prompted LLMs -- along with detailed ablations and a human evaluation study, showing SEAMUS to be a valuable benchmark for this new task.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 57 references that are in the map. First posted on arXiv 18 Oct 2024.

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References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  7. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  8. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  9. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  10. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
  11. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  12. 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
15 more
  1. 2018Generating Wikipedia by Summarizing Long Sequencescited by 333
  2. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
  3. 2021DialogSum: A Real-Life Scenario Dialogue Summarization Datasetcited by 181
  4. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 154
  5. 2017Controllable Abstractive Summarizationcited by 110
  6. 2020A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portalcited by 72
  7. 2020Multi-document Summarization via Deep Learning Techniques: A Surveycited by 58
  8. 2023Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articlescited by 45
  9. 2024A Closer Look at Claim Decompositioncited by 40
  10. 2021LOME: Large Ontology Multilingual Extractioncited by 23
  11. 2024On the Evaluation of Machine-Generated Reportscited by 23
  12. 2023SEMQA: Semi-Extractive Multi-Source Question Answeringcited by 19
  13. 2023A Unified View of Evaluation Metrics for Structured Predictioncited by 11
  14. 2022CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarizationcited by 6
  15. 2022POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Eventscited by 5

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