GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization
- URL: http://arxiv.org/abs/2410.04087v1
- Date: Sat, 5 Oct 2024 08:56:44 GMT
- Title: GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization
- Authors: Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin,
- Abstract summary: We aim to unify Multi-lingual, Cross-lingual and Multi-document Summarization into a novel task, i.e., MCMS, which encapsulates the real-world requirements all-in-one.
We meticulously constructed the GLOBESUMM dataset by first collecting a wealth of multilingual news reports and restructuring them into event-centric format.
- Score: 33.37163476772722
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: News summarization in today's global scene can be daunting with its flood of multilingual content and varied viewpoints from different sources. However, current studies often neglect such real-world scenarios as they tend to focus solely on either single-language or single-document tasks. To bridge this gap, we aim to unify Multi-lingual, Cross-lingual and Multi-document Summarization into a novel task, i.e., MCMS, which encapsulates the real-world requirements all-in-one. Nevertheless, the lack of a benchmark inhibits researchers from adequately studying this invaluable problem. To tackle this, we have meticulously constructed the GLOBESUMM dataset by first collecting a wealth of multilingual news reports and restructuring them into event-centric format. Additionally, we introduce the method of protocol-guided prompting for high-quality and cost-effective reference annotation. In MCMS, we also highlight the challenge of conflicts between news reports, in addition to the issues of redundancies and omissions, further enhancing the complexity of GLOBESUMM. Through extensive experimental analysis, we validate the quality of our dataset and elucidate the inherent challenges of the task. We firmly believe that GLOBESUMM, given its challenging nature, will greatly contribute to the multilingual communities and the evaluation of LLMs.
Related papers
- Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey [2.5459710368096586]
This survey provides a comprehensive overview of the current research on low-resource language misinformation detection.
We review the existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, real-world applications, and research efforts.
Our findings underscore the need for robust, inclusive systems capable of addressing misinformation across diverse linguistic and cultural contexts.
arXiv Detail & Related papers (2024-10-24T03:02:03Z) - A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [48.314619377988436]
The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing.
Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient.
This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
arXiv Detail & Related papers (2024-05-17T17:47:39Z) - Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers [81.47046536073682]
We present a review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature.
We hope our work can provide the community with quick access and spur breakthrough research in MLLMs.
arXiv Detail & Related papers (2024-04-07T11:52:44Z) - A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias [5.104497013562654]
We present an overview of MLLMs, covering their evolution, key techniques, and multilingual capacities.
We explore widely utilized multilingual corpora for MLLMs' training and multilingual datasets oriented for downstream tasks.
We discuss bias on MLLMs including its category and evaluation metrics, and summarize the existing debiasing techniques.
arXiv Detail & Related papers (2024-04-01T05:13:56Z) - Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles [136.84278943588652]
We propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event.
To facilitate this task, we outlined a data collection schema for identifying diverse information and curated a dataset named DiverseSumm.
The dataset includes 245 news stories, with each story comprising 10 news articles and paired with a human-validated reference.
arXiv Detail & Related papers (2023-09-17T20:28:17Z) - Mitigating Data Imbalance and Representation Degeneration in
Multilingual Machine Translation [103.90963418039473]
Bi-ACL is a framework that uses only target-side monolingual data and a bilingual dictionary to improve the performance of the MNMT model.
We show that Bi-ACL is more effective both in long-tail languages and in high-resource languages.
arXiv Detail & Related papers (2023-05-22T07:31:08Z) - Towards Unifying Multi-Lingual and Cross-Lingual Summarization [43.89340385650822]
We aim to unify multilingual summarization (MLS) and cross-lingual summarization ( CLS) into a more general setting, i.e., many-to-many summarization (M2MS)
As the first step towards M2MS, we conduct preliminary studies to show that M2MS can better transfer task knowledge across different languages than MLS and CLS.
We propose Pisces, a pre-trained M2MS model that learns language modeling, cross-lingual ability and summarization ability via three-stage pre-training.
arXiv Detail & Related papers (2023-05-16T06:53:21Z) - Cross-lingual Machine Reading Comprehension with Language Branch
Knowledge Distillation [105.41167108465085]
Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages.
We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC)
LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language.
We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
arXiv Detail & Related papers (2020-10-27T13:12:17Z) - Enhancing Answer Boundary Detection for Multilingual Machine Reading
Comprehension [86.1617182312817]
We propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision.
A mixed Machine Reading task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs.
A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web.
arXiv Detail & Related papers (2020-04-29T10:44:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.