The Art of Condensation: Constructing a State-of-the-Art Multilingual Summarization Dataset

In the field of natural language processing (NLP), the development of sophisticated models capable of understanding and generating text across a diverse array of languages is paramount. This is particularly true in the domain of abstractive summarization, where the ability to distill complex content into concise summaries is not only a technological challenge but also a linguistic one. The creation of multilingual datasets that can support the training of such models is critical, yet existing datasets often fall short in terms of both scope and depth. For instance, the XL-SUM dataset, while currently the largest for multilingual abstractive summarization with its coverage of 44 languages and over one million annotated article-summary pairs, does not encompass several significant high-resource languages such as Hungarian, Italian and German. Moreover, it typically provides only a few thousand samples per language, insufficient for deep learning models that require large amounts of data to achieve optimal performance.

Given these limitations, the aim of this paper is to address the need for a new, expansive multilingual dataset specifically tailored for abstractive summarization. Unlike prior efforts which often focus on expanding or refining existing datasets, this project proposes the construction of a completely new dataset from the ground up, one that includes a broad range of both high-resource and low-resource languages while ensuring a substantial volume of data for each. Accompanying the dataset, a novel model for abstractive summarization will be developed, designed to leverage this rich linguistic diversity effectively.

This initiative will involve the collection of sources, ensuring linguistic diversity and representativeness, and overcoming challenges associated with the cleaning in certain languages. The expected result is a balanced, comprehensive dataset that not only fills the current gaps in language coverage but also supports the development of more advanced, equitable summarization technologies. By fostering better performance across a wide spectrum of languages, this project aims to set a new standard in multilingual NLP, facilitating more accurate and culturally relevant summarizations that enhance understanding and accessibility worldwide.

Meltwater is a premier international provider of media intelligence and social analytics. This organization systematically processes hundreds of millions of documents daily, sourced from a diverse array of online platforms including blogs, forums, news articles, and social media postings. These documents undergo enhancement through several sophisticated algorithms that facilitate language detection, sentiment analysis, content classification, and author qualification. Over the years, Meltwater has amassed a substantial repository of ingresses—concise summaries that encapsulate the essence of editorial content—from web pages of leading news outlets such as the BBC. These ingresses, often authored by the original writers of the articles, represent a multilingual collection due to Meltwater's comprehensive web coverage. Despite their inherent imperfections, these data samples hold significant potential for advancing the state of the art in multilingual abstractive summarization. If leveraged effectively, Meltwater’s rich datasets could markedly enhance performance in multilingual summarization tasks.