Legal documents are notorious for their length and complexity, making it challenging to extract crucial information efficiently. In this paper, we introduce a new dataset for legal text summarization, covering 24 languages. We not only present and analyze the dataset but also conduct experiments using various extractive techniques. We provide a comparison between these techniques and summaries generated by the state-of-the-art GPT models. The abstractive GPT approach outperforms the extractive TextRank approach in 8 languages, but produces slightly lower results in the remaining 16 languages. This research aims to advance the field of legal document summarization by addressing the need for accessible and comprehensive information retrieval from lengthy legal texts.

Extracting the most important part of legislation documents has great business value because the texts are usually very long and hard to understand. The aim of this article is to evaluate different algorithms for text summarization on EU legislation documents. The content contains domain-specific words. We collected a text summarization dataset of EU legal documents consisting of 1563 documents, in which the mean length of summaries is 424 words. Experiments were conducted with different algorithms using the new dataset. A simple extractive algorithm was selected as a baseline. Advanced extractive algorithms, which use encoders show better results than baseline. The best result measured by ROUGE scores was achieved by a fine-tuned abstractive T5 model, which was adapted to work with long texts.