Cross-lingual Science Journalism: Select, Simplify and Rewrite Summaries for Non-expert Readers
Mehwish Fatima, Michael Strube
Abstract
Automating Cross-lingual Science Journalism (CSJ) aims to generate popular science summaries from English scientific texts for nonexpert readers in their local language. We introduce CSJ as a downstream task of text simplification and cross-lingual scientific summarization to facilitate science journalists' work. We analyze the performance of possible existing solutions as baselines for the CSJ task. Based on these findings, we propose to combine the three components -SELECT, SIMPLIFY and REWRITE (SSR) to produce cross-lingual simplified science summaries for non-expert readers. Our empirical evaluation on the WIKIPEDIA dataset shows that SSR significantly outperforms the baselines for the CSJ task and can serve as a strong baseline for future work. We also perform an ablation study investigating the impact of individual components of SSR. Further, we analyze the performance of SSR on a high-quality, real-world CSJ dataset with human evaluation and in-depth analysis, demonstrating the superior performance of SSR for CSJ. A Scientific and News Structure Figure A.1 presents the difference between a scientific text discourse and a news text discourse.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 4dfd571e-c63c-44bc-8a50-25f6972f3131Cited by top-tier papers2
- Evaluating LLMs for Targeted Concept Simplification for Domain-Specific TextsSumit Asthana, Hannah Rashkin, Elizabeth Clark, Fantine Huot et al.EMNLP 2024 · 2 citations
- What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific PresentationsDongqi Liu, Chenxi Whitehouse, Xi Yu, Louis Mahon et al.ACL 2025
Builds on8
- BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger et al.ICLR 2020 · 8,443 citations
- PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive SummarizationJingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. LiuICML 2020 · 2,453 citations
- On Extractive and Abstractive Neural Document Summarization with Transformer Language ModelsJonathan Pilault, Raymond Li, Sandeep Subramanian, Chris PalEMNLP 2020 · 186 citations
- Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document SummarizationHanqi Jin, Tianming Wang, Xiaojun WanACL 2020 · 92 citations
- On Faithfulness and Factuality in Abstractive SummarizationJoshua Maynez, Shashi Narayan, Bernd Bohnet, Ryan T. McDonaldACL 2020 · 54 citations
Related papers
- We Can Explain Your Research in Layman's Terms: Towards Automating Science Journalism at ScaleRumen Dangovski, Michelle Shen, Dawson Byrd, Li Jing et al.AAAI 2021 · 12 citations
- 'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Automatic Science JournalismRonald Cardenas, Bingsheng Yao, Dakuo Wang, Yufang HouEMNLP 2023 · 2 citations
- SIMSUM: Document-level Text Simplification via Simultaneous SummarizationSofia Blinova, Xinyu Zhou, Martin Jaggi, Carsten Eickhoff et al.ACL 2023 · 11 citations
- Keep It Simple: Unsupervised Simplification of Multi-Paragraph TextPhilippe Laban, Tobias Schnabel, Paul N. Bennett, Marti A. HearstACL 2021
- Making Science Simple: Corpora for the Lay Summarisation of Scientific LiteratureTomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina ScartonEMNLP 2022 · 38 citations
