M2DS: Multilingual Dataset for Multi-document Summarisation

• URL: http://arxiv.org/abs/2407.12336v1
• Date: Wed, 17 Jul 2024 06:25:51 GMT
• Title: M2DS: Multilingual Dataset for Multi-document Summarisation
• Authors: Kushan Hewapathirana, Nisansa de Silva, C. D. Athuraliya,
• Abstract summary: Multi-document Summarisation (MDS) has resulted in diverse datasets covering customer reviews, academic papers, medical and legal documents, and news articles. However, the English-centric nature of these datasets has created a conspicuous void for multilingual datasets in today's globalised digital landscape. This paper introduces M2DS, emphasising its unique multilingual aspect, and includes baseline scores from state-of-the-art MDS models evaluated on our dataset.
• Score: 0.5071800070021028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the rapidly evolving digital era, there is an increasing demand for concise information as individuals seek to distil key insights from various sources. Recent attention from researchers on Multi-document Summarisation (MDS) has resulted in diverse datasets covering customer reviews, academic papers, medical and legal documents, and news articles. However, the English-centric nature of these datasets has created a conspicuous void for multilingual datasets in today's globalised digital landscape, where linguistic diversity is celebrated. Media platforms such as British Broadcasting Corporation (BBC) have disseminated news in 20+ languages for decades. With only 380 million people speaking English natively as their first language, accounting for less than 5% of the global population, the vast majority primarily relies on other languages. These facts underscore the need for inclusivity in MDS research, utilising resources from diverse languages. Recognising this gap, we present the Multilingual Dataset for Multi-document Summarisation (M2DS), which, to the best of our knowledge, is the first dataset of its kind. It includes document-summary pairs in five languages from BBC articles published during the 2010-2023 period. This paper introduces M2DS, emphasising its unique multilingual aspect, and includes baseline scores from state-of-the-art MDS models evaluated on our dataset.

Related papers

• Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages [55.36534539177367]
  This paper introduces Pangea, a multilingual multimodal large language model (MLLM) trained on a diverse 6M instruction dataset spanning 39 languages. P Pangea significantly outperforms existing open-source models in multilingual settings and diverse cultural contexts. We fully open-source our data, code, and trained checkpoints, to facilitate the development of inclusive and robust multilingual MLLMs.
  arXiv  Detail & Related papers  (2024-10-21T16:19:41Z)
• A Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Model [15.596156608713347]
  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.
  arXiv  Detail & Related papers  (2024-10-13T08:15:33Z)
• UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset [69.33424532827608]
  Open-source large language models (LLMs) have gained significant strength across diverse fields. In this work, we construct an open-source multilingual supervised fine-tuning dataset. The resulting UltraLink dataset comprises approximately 1 million samples across five languages.
  arXiv  Detail & Related papers  (2024-02-07T05:05:53Z)
• Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval [62.82448161570428]
  This dataset is designed to investigate fairness in a multilingual information retrieval context. It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages. It offers rich demographic information associated with its documents, facilitating the study of demographic bias.
  arXiv  Detail & Related papers  (2023-11-03T12:29:11Z)
• MMT: A Multilingual and Multi-Topic Indian Social Media Dataset [1.0413233169366503]
  Social media plays a significant role in cross-cultural communication. A vast amount of this occurs in code-mixed and multilingual form. We introduce a large-scale multilingual, and multi-topic dataset (MMT) collected from Twitter.
  arXiv  Detail & Related papers  (2023-04-02T21:39:00Z)
• MUTANT: A Multi-sentential Code-mixed Hinglish Dataset [16.14337612590717]
  We propose a novel task of identifying multi-sentential code-mixed text (MCT) from multilingual articles. As a use case, we leverage multilingual articles and build a first-of-its-kind multi-sentential code-mixed Hinglish dataset. The MUTANT dataset comprises 67k articles with 85k identified Hinglish MCTs.
  arXiv  Detail & Related papers  (2023-02-23T04:04:18Z)
• Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages [62.730361829175415]
  MIRACL is a multilingual dataset we have built for the WSDM 2023 Cup challenge. It focuses on ad hoc retrieval across 18 different languages. Our goal is to spur research that will improve retrieval across a continuum of languages.
  arXiv  Detail & Related papers  (2022-10-18T16:47:18Z)
• GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems [66.92182084456809]
  We introduce a novel data curation method that generates GlobalWoZ -- a large-scale multilingual ToD dataset from an English ToD dataset. Our method is based on translating dialogue templates and filling them with local entities in the target-language countries. We release our dataset as well as a set of strong baselines to encourage research on learning multilingual ToD systems for real use cases.
  arXiv  Detail & Related papers  (2021-10-14T19:33:04Z)
• M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations [72.81164101048181]
  We propose a dataset for Multimodal Multiparty Hindi Humor (M2H2) recognition in conversations containing 6,191 utterances from 13 episodes of a very popular TV series "Shrimaan Shrimati Phir Se" Each utterance is annotated with humor/non-humor labels and encompasses acoustic, visual, and textual modalities. The empirical results on M2H2 dataset demonstrate that multimodal information complements unimodal information for humor recognition.
  arXiv  Detail & Related papers  (2021-08-03T02:54:09Z)
• MLSUM: The Multilingual Summarization Corpus [29.943949944682196]
  MLSUM is the first large-scale MultiLingual SUMmarization dataset. It contains 1.5M+ article/summary pairs in five different languages.
  arXiv  Detail & Related papers  (2020-04-30T15:58:34Z)

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.