Cross-lingual timeline summarization

Abstract

Timeline summarization methods analyze times-tamped, topic-specific news article collections to select the key dates representing the event flow and to extract the most relevant per-date content. Existing approaches are all tailored to a single language. Hence, they are unable to combine topic-related content available in different languages. Enriching news timelines with multilingual content is particularly useful for (i) summarizing complex events, whose main facets are covered differently by media sources from different countries, and (ii) generating news timelines in low-resource languages, for which there is a lack of news content in the target language.This paper presents three alternative approaches to address cross-lingual timeline summarization. They combine state-of-the-art extractive summarization methods with machine translation steps at different stages of the timeline generation process. The paper also proposes novel Rouge-based evaluation metrics customized for cross-lingual timeline summarization with a twofold aim: (i) quantifying the ability of the cross-lingual process to enhance available content extraction in the target language and (ii) estimating summarizer effectiveness in conveying additional content from other languages. A new multilingual timeline benchmark dataset has been generated to allow a thorough analysis of the factors that mainly influence summarization performance.