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

TL;DR

This paper introduces a new dataset for mixed-language multi-document news summarization and proposes a graph-based extract-generate model to improve summarization in multilingual, multi-document scenarios.

Contribution

The paper creates the first large-scale MLMD-news dataset and develops a novel graph-based model, advancing research in multilingual multi-document summarization.

Findings

Benchmarking various methods on MLMD-news dataset

Demonstrating effectiveness of the proposed graph-based model

Public release of dataset and code for research community

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 a 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\footnote[1]{https://github.com/Southnf9/MLMD-news}, aiming to advance research in summarization within MLMD scenarios.