The ready availability of machine translation (MT) systems such as Google Translate has
profoundly changed how society engages with multilingual communication practices. In …

This chapter addresses machine translation (MT) with an eye to legal terminology. The
translation of legal terms and phrasemes may be fraught with contextual complexities, and …

Summarization, like other natural language processing tasks, is tackled with a range of
different techniques-particularly machine learning approaches, where human intuition goes …

Limited research has addressed the use of machine translation (MT) with legal texts, while
recent data-driven MT approaches have improved quality. The present study reports on the …

The paper reports on the creation, annotation and curation of the MT@ BZ corpus, a
bilingual (Italian–South Tyrolean German) corpus of machine-translated legal texts from the …

This paper presents a new approach to building legal citation classification systems. Our
approach is based on Ripple-down Rules (RDR), an efficient knowledge acquisition …

This paper presents the challenges and possibilities of a novel summarisation task:
automatic generation of catchphrases for legal documents. Catchphrases are meant to …

In the past couple of decades, machine translation (MT), whether phrase-based statistical or
neural MT, has made considerable progress and has become increasingly common in …

The idea of automatic summarization dates back to 1958, when Luhn invented the “auto
abstract”(Luhn, 1958). Since then, many diverse automatic summarization approaches have …

Following the implementation of South Tyrol's Statute of Autonomy, the public
administrations of the Autonomous Province of Bozen/Bolzano are legally bound to the …