A Multilingual Study of Compressive Cross-Language Text Summarization
This addresses the issue of inconsistent summary quality in multilingual CLTS for users needing reliable cross-language summaries, though it appears incremental as it builds on existing methods.
The paper tackled the problem of performance variability in cross-language text summarization (CLTS) by proposing a compressive framework, which outperformed extractive state-of-the-art methods with better and more stable ROUGE scores across four languages.
Cross-Language Text Summarization (CLTS) generates summaries in a language different from the language of the source documents. Recent methods use information from both languages to generate summaries with the most informative sentences. However, these methods have performance that can vary according to languages, which can reduce the quality of summaries. In this paper, we propose a compressive framework to generate cross-language summaries. In order to analyze performance and especially stability, we tested our system and extractive baselines on a dataset available in four languages (English, French, Portuguese, and Spanish) to generate English and French summaries. An automatic evaluation showed that our method outperformed extractive state-of-art CLTS methods with better and more stable ROUGE scores for all languages.