Article ID: 2024EDP7274
Cross-lingual summarization (CLS) simplifies obtaining information across languages by generating summaries in the target language from source documents in another. State-of-the-art neural summarization models typically rely on training or fine-tuning with extensive corpora. Nonetheless, applying these approaches in practical industrial scenarios poses challenges due to the scarcity of annotated data. Recent research utilizes large language models (LLMs) to generate superior summaries by extracting fine-grained elements (entities, dates, events, and results) from source documents based on the Chain of Thought (CoT). Such an approach inevitably leads to the loss of fact-relationship across elements in the original document, thus hurting the performance of summary generation. In this paper, we not only substantiate the importance of the fact-relationship across elements for summary generation on the element-aware test sets CNN/DailyMail and BBC XSum but also propose a novel Cross-Lingual Summarization method based on Element Fact-relationship Generation (EFGCLS). Specifically, we break down the CLS task into three simple subtasks: though element fact-relationship generation extracts fine-grained elements in source documents and the fact-relationship across them; afterwards the monolingual document summarization leverages the fact-relationship and source documents to generate the monolingual summary; ultimately, the cross-lingual summarization via Cross-lingual Prompting (CLP) enhance the alignment between source language summaries and target language summaries. Experimental results on the element-aware datasets show that our method outperforms state-of-the-art fine-tuned PLMs and zero-shot LLMs by +6.28/+1.22 in ROUGE-L, respectively.
