Extractive text summarisation of Privacy Policy documents using machine
  learning approaches
Chanwoo Choi
This work demonstrates two Privacy Policy (PP) summarisation models based on
two different clustering algorithms: K-means clustering and Pre-determined
Centroid (PDC) clustering. K-means is decided to be used for the first model
after an extensive evaluation of ten commonly used clustering algorithms. The
summariser model based on the PDC-clustering algorithm summarises PP documents
by segregating individual sentences by Euclidean distance from each sentence to
the pre-defined cluster centres. The cluster centres are defined according to
General Data Protection Regulation (GDPR)'s 14 essential topics that must be
included in any privacy notices. The PDC model outperformed the K-means model
for two evaluation methods, Sum of Squared Distance (SSD) and ROUGE by some
margin (27% and 24% respectively). This result contrasts the K-means model's
better performance in the general clustering of sentence vectors before running
the task-specific evaluation. This indicates the effectiveness of operating
task-specific fine-tuning measures on unsupervised machine-learning models. The
summarisation mechanisms implemented in this paper demonstrates an idea of how
to efficiently extract essential sentences that should be included in any PP
documents. The summariser models could be further developed to an application
that tests the GDPR-compliance (or any data privacy legislation) of PP
documents.