A Novel Wikipedia based Dataset for Monolingual and Cross-Lingual Summarization

Abstract

Cross-lingual summarization is a challenging task for which there are no cross-lingual scientific resources currently available. To overcome the lack of a high-quality resource, we present a new dataset for monolingual and cross-lingual summarization considering the English-German pair. We collect high-quality, real-world cross-lingual data from Spektrum der Wissenschaft, which publishes humanwritten German scientific summaries of English science articles on various subjects. The generated Spektrum dataset is small; therefore, we harvest a similar dataset from the Wikipedia Science Portal to complement it. The Wikipedia dataset consists of English and German articles, which can be used for monolingual and cross-lingual summarization. Furthermore, we present a quantitative analysis of the datasets and results of empirical experiments with several existing extractive and abstractive summarization models. The results suggest the viability and usefulness of the proposed dataset for monolingual and crosslingual summarization.

SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism

“…Cross-lingual scientific summarization is an understudied area due to its challenging nature. We find two studies: a synthetic dataset from English to Somali, Swahili, and Tagalog with round trip translation (Ouyang et al, 2019), two real cross-lingual datasets from Wikipedia Science Portal and Spektrum der Wissenschaft for English-German (Fatima and Strube, 2021).…”

“…These datasets are, unfortunately, not suitable for cross-lingual science journalism. Moreover, crosslingual science journalism has been investigated as a fusion of cross-lingual summarization and text simplification with a pipeline model (Fatima and Strube, 2023) with cross-lingual scientific datasets (Fatima and Strube, 2021). In the dawn of cross-lingual summarization, various pipeline models (Ouyang et al, 2019;Zhu et al, 2019Zhu et al, , 2020 with synthetic cross-lingual datasets have been introduced to explore the task.…”

“…In the dawn of cross-lingual summarization, various pipeline models (Ouyang et al, 2019;Zhu et al, 2019Zhu et al, , 2020 with synthetic cross-lingual datasets have been introduced to explore the task. Later, cross-lingual summarization models have been focused towards Multi-Task Learning (MTL) (Cao et al, 2020;Bai et al, 2021Bai et al, , 2022 and direct cross-lingual summarization with non-synthetic datasets (Ladhak et al, 2020;Fatima and Strube, 2021).…”

X-Scitldr

“…The work closest to ours has been recently presented by Fatima and Strube [16], who introduce an English-German cross-lingual summarization dataset collected from German scientific magazines and Wikipedia. This resource is complementary to ours in many different aspects.…”

“…Our dataset consists of two main portions: a) a translated version of the original dataset from Cachola et al [5] in German, Italian and Chinese to enable comparability across languages on the basis of post-edited automatic translations; b) a dataset of human-generated TLDRs in Japanese from a community-based summarization platform to test performance on a second, comparable human-generated dataset. Our work complements seminal efforts from Fatima and Strube [16], who compile an English-German cross-lingual dataset from the Spektrum der Wissenschaft / Scientific American and Wikipedia, in that we focus on extreme summarization, build a dataset of expertderived multilingual TLDRs (as opposed to leads from Wikipedia), and provide additional languages.…”