A Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Model (2025)

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 an 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, aiming to advance research in summarization within MLMD scenarios.

## introduction the news summarization task aims to simplify and condense a large volume of news content through automated methods, extracting key information and main viewpoints so that readers can quickly grasp the core content of the news. existing research on news summarization primarily focuses on single-language single-document (slsd) @xcite @xcite , single-language multidocument (slmd) @xcite @xcite and cross-language single-document (clsd) @xcite @xcite . however, in reality, many news articles, especially international news, appear in the form of mixed-figure 1 : the diagram of slsd, slmd, clsd and mlmd. each rounded rectangle represents a source document, while the pointed rectangle represents the target summary. "en", "de", "fr" and "es" indicate that the text is in english, german, french, and spanish, respectively. it is noteworthy that, with the advancement of multi-language models such as mbart @xcite and gpt @xcite , a task referred to as multilanguage multi-document news summarization has recently emerged @xcite @xcite . in the task, although the languages of different source document clusters vary, each individual source document cluster consists of multiple documents in the same language. therefore, in each instance of summary generation for this type of task, it essentially falls under the category slmd. in contrast, in mlmd, each individual source document cluster is composed of multiple documents in different languages. from this perspective, mlmd is more challenging than multi-language multi-document. the latter requires the model to have the capability to understand multiple documents in the current language during a single summary generation. in contrast, mlmd requires the model to simultaneously possess the ability to understand multiple languages and multiple documents within a single summary generation. however, the lack of mlmd news datasets has hindered progress in this field. therefore, we first construct a mlmd-news dataset. this dataset includes documents in four languages: english, german, french, and spanish, with a total of 10,992 source document clusters and corresponding target summaries. each source document cluster is composed of multiple documents in different languages, and the corresponding target summary is in english. additionally, we propose a graph-based extract-generate model for the mlmd task. this model first uses an extractor based on graph neural networks to extract key sentences from a source document cluster, and then employs a generator based on pre-trained models to generate the target summary based on these key sentences. finally, we benchmark various methods on the mlmd-news dataset and publicly release our dataset and code to advance research in summarization within mlmd scenarios. the contributions of this paper are summarized as follows: ## related work the related work in news summarization research primarily focuses on three areas, as detailed below: single-language single-document summarization (slsd): as shown in figure 1 .(a), the slsd news summarization task takes a source document as input and outputs a target summary in the same language. existing methods are mainly divided into two categories: extractive and abstractive. extractive summarization constructs the target summary by directly selecting key sentences or paragraphs from the source document, such as tex-trank @xcite and deepsumm @xcite . abstractive summarization, on the other hand, involves first understanding the content of the source document and then generating new summary sentences for the target summary, such as bertsum @xcite and cogitoergosumm @xcite . cross-language single-document summarization (clsd): as shown in figure 1 .(b), the clsd news summarization task takes a source document as input and produces a target summary in a different language. existing research is primarily divided into pipeline-based and endto-end. traditional clsd methods typically use a pipeline-based methods @xcite , where the source document is first translated and then summarized, or the summary is generated first and then translated into the target language. in recent years, researchers have increasingly focused on end-to-end clsd methods @xcite , which can directly generate summaries in the target language, significantly reducing the risk of error propagation. single-language multi-document summarization (slmd): as shown in figure 1 .(c), the slmd news summarization task takes a source document cluster as input, which contains multiple documents, and the output is a target summary in the same language. existing methods can be categorized into extractive, abstractive, and hybrid. in the early days, due to the small sample size of slmd datasets like duc 2004 @xcite , research on multi-document summarization primarily relied on extractive methods @xcite . in recent years, the emergence of large-scale slmd datasets, such as multi-news @xcite , has accelerated the development of abstractive @xcite and hybrid slmd news summarization @xcite @xcite . recently, with the development of multilanguage models such as mbart @xcite and gpt @xcite , a task known as multilanguage multi-document news summarization @xcite @xcite has emerged within the slmd paradigm. this task involves inputs and outputs similar to those in slmd, where a source document cluster is input and a target summary in the same language is produced. the difference lies in that the languages of different source document clusters can figure 2 : the diagram illustrates the construction process of the mlmd-news dataset. first, a round-trip translation (rtt) strategy is employed to translate each news document in the source document clusters of the multi-news dataset into multiple languages and then back into the original language. this process allows the calculation of the rouge-1 score matrix for the document cluster. based on this score matrix, a greedy selection strategy is used to assign a corresponding language to each news document. the original content of the news document is then replaced with the translated content in the assigned language, resulting in a source document cluster with mixed languages. ## the mlmd-news dataset the overall process of constructing the mlmdnews dataset is illustrated in figure 2 . the mlmdnews dataset is built upon the multi-news dataset, which is a well-known and widely used english multi-document summarization dataset. the construction process employs a round-trip translation strategy and a greedy selection strategy, to approximate real-world cases. similar methods, like the ncls dataset @xcite in cross-language summarization, have been widely accepted and used. the main goal of the round-trip translation strategy is to calculate a rouge-1 score matrix that reflects translation quality, while the greedy selection strategy is used to assign the most suitable language to each news document in the source document cluster and make the necessary replacements. ## graph-based extract-generate model to the best of our knowledge, there is currently no method specifically designed for the mlmd task. the input for mlmd news summarization consists of multiple mixed-language documents, which presents two main challenges: the excessive length of the input and the complex relationships between multiple documents and languages. therefore, we propose a graph-based extractive-generative model (as shown in figure 5 ) as a baseline for this task. the extract-then-generate approach addresses the issue of long input, while the graph is used to model the complex relationships between multiple documents and languages. the model consists of three main modules: graph construction, extractor, and generator. this section will provide a detailed explanation of these three modules. ## experiments in this section, we will introduce the baselines we used and present the implementation details. ## conclusion in this paper, we constructed the first mixedlanguage multi-document news summarization dataset (mlmd-news) and proposed a graph-based extract-generate model specifically designed for the mlmd news summarization task. we conducted benchmark tests on the mlmd-news dataset, evaluating our proposed method along with advanced methods such as llm. additionally, we have publicly released the dataset and code, hoping to foster further development in the mlmd news summarization area.