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

• URL: http://arxiv.org/abs/2410.09773v1
• Date: Sun, 13 Oct 2024 08:15:33 GMT
• Title: A Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Model
• Authors: Shengxiang Gao, Fang nan, Yongbing Zhang, Yuxin Huang, Kaiwen Tan, Zhengtao Yu,
• Abstract summary: In real-world scenarios, news about an international event often involves multiple documents in different languages. We construct a mixed-language multi-document news summarization dataset (MLMD-news) This dataset contains four different languages and 10,992 source document cluster and target summary pairs.
• Score: 15.596156608713347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 code\footnote[1]{https://github.com/Southnf9/MLMD-news}, aiming to advance research in summarization within MLMD scenarios.

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