Headnote Prediction Using Machine Learning
Sarmad Mahar1, Sahar Zafar2,
and Kamran Nishat1
1CoCIS, PAF-Karachi
Institute of Economics and Technology, Pakistan
2Computer Science,
Sindh Madressatul Islam University, Pakistan
Abstract: Headnotes
are the precise explanation and summary of legal points in an issued judgment.
Law journals hire experienced lawyers to write these headnotes. These headnotes
help the reader quickly determine the issue discussed in the case. Headnotes
comprise two parts. The first part comprises the topic discussed in the
judgment, and the second part contains a summary of that judgment. In this
thesis, we design, develop and evaluate headnote prediction using machine
learning, without involving human involvement. We divided this task into a two
steps process. In the first step, we predict law points used in the judgment by
using text classification algorithms. The second step generates a summary of
the judgment using text summarization techniques. To achieve this task, we
created a Databank by extracting data from different law sources in Pakistan.
We labelled training data generated based on Pakistan law websites. We tested
different feature extraction methods on judiciary data to improve our system.
Using these feature extraction methods, we developed a dictionary of
terminology for ease of reference and utility. Our approach achieves 65%
accuracy by using Linear Support Vector Classification with tri-gram and
without stemmer. Using active learning our system can continuously improve the
accuracy with the increased labelled examples provided by the users of the
system.
Keywords: Judgment summary, head-note
prediction, machine learning, text summarization.
Received March 6, 2020; accepted September
17, 2020