mlsanityData to 7 Oct 2026

A Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Model

arXiv 2410.09773 ↗First posted 13 Oct 2024cs.CLcited by 2

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Abstract (arXiv)

Existing research on news summarization primarily focuses on single-language single-document (SLSD), single-language multi-document (SLMD) or cross-language single-document (CLSD). However, in real-world scenarios, news about a international event often involves multiple documents in different languages, i.e., mixed-language multi-document (MLMD). Therefore, summarizing MLMD news is of great significance. However, the lack of datasets for MLMD news summarization has constrained the development of research in this area. To fill this gap, we construct a mixed-language multi-document news summarization dataset (MLMD-news), which contains four different languages and 10,992 source document cluster and target summary pairs. Additionally, we propose a graph-based extract-generate model and benchmark various methods on the MLMD-news dataset and publicly release our dataset and codefootnote[1]https://github.com/Southnf9/MLMD-news, aiming to advance research in summarization within MLMD scenarios.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 42 references that are in the map. First posted on arXiv 13 Oct 2024.

Builds on

14 of 42 references.

References · 14

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2017Graph Attention Networkscited by 8,854
  4. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
  5. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  6. 2019Text Summarization with Pretrained Encoderscited by 553
  7. 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
  8. 2020Multilingual Translation with Extensible Multilingual Pretraining and Finetuningcited by 287
  9. 2020Heterogeneous Graph Neural Networks for Extractive Document Summarizationcited by 90
  10. 2019NCLS: Neural Cross-Lingual Summarizationcited by 64
  11. 2017Graph-based Neural Multi-Document Summarizationcited by 54
  12. 2021DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarizationcited by 34
2 more
  1. 2022Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awarenesscited by 13
  2. 2015Multi-Document Summarization via Discriminative Summary Rerankingcited by 6

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Graph-Guided Passage Retrieval for Author-Centric Structured Feedbackcited by 3
  2. 2025Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languagescited by 0