# Extractive text summarisation of Privacy Policy documents using machine learning approaches

arXiv: https://arxiv.org/abs/2404.08686 · HTML: https://mlsanity.com/paper/2404.08686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Neural Document Summarization by Jointly Learning to Score and Select Sentences](https://mlsanity.com/paper/1807.02305/) (2018)
- [Deep Communicating Agents for Abstractive Summarization](https://mlsanity.com/paper/1803.10357/) (2018)
- [Neural Latent Extractive Document Summarization](https://mlsanity.com/paper/1808.07187/) (2018)
- [A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids](https://mlsanity.com/paper/1902.00220/) (2019)
- [Selecting the number of components in PCA via random signflips](https://mlsanity.com/paper/2012.02985/) (2020)
- [Incorporating BERT into Neural Machine Translation](https://mlsanity.com/paper/2002.06823/) (2020)

## Abstract (arXiv)

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.