Multi-language transfer learning for low-resource legal case summarization

Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming and requires excellent human expertise. Moreover, public legal corpora of specific languages are almost unavailable. This paper proposes a transfer learning approach with extractive and abstractive techniques to cope with the lack of labeled legal summarization datasets, namely a low-resource scenario. In particular, we conducted extensive multi- and cross-language experiments. The proposed work outperforms the state-of-the-art results of extractive summarization on the Australian Legal Case Reports dataset and sets a new baseline for abstractive summarization. Finally, syntactic and semantic metrics assessments have been carried out to evaluate the accuracy and the factual consistency of the machine-generated legal summaries

Identification of Decision Rules from Legislative Documents Using Machine Learning and Natural Language Processing

Decision logic extraction from natural language texts can be a tedious, labor-intensive task. This is especially true for legislative texts, since they do not always follow usual speech and writing patterns. This paper explores the possibility of using machine learning and natural language processing approaches to identify decision rules within legislative documents, and ultimately provides the possibility of building an extraction algorithm on top of the solution to extract and visualize decision logic automatically. Such a novel method for decision rules identification bears the potential to reduce human labor, minimize mistakes, and lessen context dependency. To accomplish this, we use pre-trained word vectorization in conjunction with a complex multi-layer convolutional neural network (CNN). The relevant data used in this project was generated from the Austrian income tax code and labeled by hand. A quantitative evaluation shows that our approach can be trained on as little as a single code of law and still obtain significant accuracy

Semantic Text Analysis tool: SeTA

An ever-growing number and length of documents, number and depth of topics covered by legislation, and ever new phrases and their slowly changing meaning, these are all contributing factors that make policy analysis more and more complex. As implication, human policy analysts and policy developers face increasing entanglement of both content and semantical levels. To overcome several of these issues, JRC has developed a central pilot tool called AI-KEAPA to support policy analysis and development in any domain. Recent developments in big data, machine learning and especially in natural language processing allow converting unfathomable complexity of many hundreds of thousands of documents into a normalised high-dimensional vector space preserving the knowledge. Unstructured text in document corpora and big data sources, until recently considered just an archive, is quickly becoming core source of analytical information using text mining methods to extract qualitative and quantitative data. Semantic analysis allows us to extract better information for policy analysis from metadata titles and abstracts than from the structured human-entered descriptions. This digital assistant allows document search and extraction over many different sources, discovery of phrase meaning, context and temporal development. It can recommend most relevant documents including their semantic and temporal interdependencies. But most importantly, it helps bursting knowledge bubbles and fast-learning new domains. This way we hope to mainstream artificial intelligence into policy support. The tool is now fit for purpose. It was thoroughly tested in real-life conditions for about two years mainly in the area of legislative impact assessments for policy formulation, and other domains such as large data infrastructure analysis, agri-environmental measures or natural disasters, some of which are detailed in this document. This approach boosts the strategic JRC focus on application of scientific analysis and development. This service adds to the JRC competence and central position in semantic reasoning for policy analysis, active information recommendation, and inferred knowledge in policy design and development.JRC.I.3-Text and Data Minin