Summary of Globesumm: a Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization, by Yangfan Ye et al.
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization
by Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of summarizing news in a multilingual setting, where various languages and viewpoints are present. The current state-of-the-art approaches often focus on single-language or single-document tasks, neglecting real-world scenarios. To bridge this gap, the authors propose a novel task called Multi-lingual, Cross-lingual, and Multi-document Summarization (MCMS), which combines these challenges into one task. However, the lack of a benchmark hinders researchers from studying this problem adequately. To address this, the authors construct the GLOBESUMM dataset by collecting multilingual news reports and restructuring them into event-centric format. They also introduce protocol-guided prompting for high-quality reference annotation. The paper highlights the challenges of conflicts between news reports, redundancies, and omissions in MCMS, which adds to the complexity of GLOBESUMM. Through experimental analysis, the authors validate the quality of their dataset and elucidate the inherent challenges of the task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research tries to make it easier for machines to summarize news from different languages and many different articles. Right now, most machine learning models are only good at summarizing one language or one article. But in real life, we get news from all over the world, in different languages, and there’s a lot of information. The researchers created a new task called MCMS that tries to summarize all these things together. They also made a special dataset called GLOBESUMM with lots of news articles in many different languages. This will help machines learn how to summarize real-world news better. |
Keywords
» Artificial intelligence » Machine learning » Prompting » Summarization
