  Legal practitioners often face a vast amount of documents. Lawyers, for
instance, search for appropriate precedents favorable to their clients, while
the number of legal precedents is ever-growing. Although legal search engines
can assist finding individual target documents and narrowing down the number of
candidates, retrieved information is often presented as unstructured text and
users have to examine each document thoroughly which could lead to information
overloading. This also makes their statistical analysis challenging. Here, we
present an end-to-end information extraction (IE) system for legal documents.
By formulating IE as a generation task, our system can be easily applied to
various tasks without domain-specific engineering effort. The experimental
results of four IE tasks on Korean precedents shows that our IE system can
achieve competent scores (-2.3 on average) compared to the rule-based baseline
with as few as 50 training examples per task and higher score (+5.4 on average)
with 200 examples. Finally, our statistical analysis on two case
categories--drunk driving and fraud--with 35k precedents reveals the resulting
structured information from our IE system faithfully reflects the macroscopic
features of Korean legal system.
