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Structural Text Segmentation of Legal Documents

by Dennis Aumiller*, Satya Almasian*, Sebastian Lackner and Michael Gertz

*Equal Contribution.

Online Models

You can now directly load the PyTorch weights in Huggingface transformers for the Model Roberta CLS consec (run 5): This was the best-performing run on base transformers (not sentence-transformers).
Update 2021-09-30: We have now a model trained on Wikipedia paragraphs instead, thanks in large parts to Lucienne-Sophie Marmé. The model can be found online on the Huggingface model hub:
Update 2022-02-16: We have made some changes to our sentence-transformers fork. If you use our tool, please check out the topical-change branch of our fork!

This repository contains scripts to reproduce the results of the paper, for transformer based models for text segmentation.

This repository contains mostly data generation and evaluation scripts. For pre-trained models used in the paper, please reach out to the authors via <lastname>


Our Terms-of-Service dataset is publicly available at For more information on the data please refer to the paper and appendix. The dataset consist of json files for Terms of Services crawled from the web. An example file structure can be seen here:

    "text": "a paragraph of the terms of services",
    "section": [list of all the headings and subheading that the paragraph belongs to]

Training from Scratch

This repository does not include training scripts to train the models on your own data:

  • sentence-transformer: please refer to our forked project sentence-transformer. Please make sure to check out the topical-change branch. All the training scripts for baselines, such as Bag of Words, average of GloVe and tf-idf can be found in the segmenation_baseline folder. The Sentence-Transformer-based RoBERTa and BERT can be trained using the scripts in segmenation_models.
  • transformer-language models: please refer to our forked project transformers.
  • GraphSeg: The GraphSeg model is available in Java and can be accessed here. For our evaluation we ran the standalone binary provided in the repository, with a minimum section length of two sentences, and a segmentation threshold of 0.1-0.5 in increments of 0.1.
  • WikiSeg: Implementation of the paper Text Segmentation as a Supervised Learning Task can be accessed here. Note that this project has different requirements.


Our code has been run on both python 3.7 and python 3.8, but in order to run the evaluation script for the baseline model of textseg you need to switch to python 2, since their model is trained and loaded with python 2 packages. Basically, for the script 03_test_textseg to work, you need to switch to the environment from the WikiSeg repository. For the remaining code, please refer to our requirements:

pip install -r requirements.txt


Step one (data cleaning):

After downloading the dataset, you should start by cleaning the files and filtering them to the relative topics, using our_model/ This script takes the raw data and filters out the irrelevant sections and paragraphs. The topics and the heading associations are taken from a dictionary in our_model/ Example:

python3 our_model/ --paragraph  True --input_folder INPUT_FOLDER --output_folder OUTPUT_FOLDER                   

if paragraph is set to True, then the paragraph structure is kept. Otherwise they are merged into a single section. input_folder is the location of the terms of services dataset.

Step two (generating training data):

To generate training pair with different training strategies mentioned in the paper, use our_model/ For the baselines there are also respective scripts in: baseline/ and baseline/ Example:

python our_model/ --input_folder INPUT_FOLDER --output_folder OUTPUT_FOLDER \
  --sentence_tokenizer_path SENTENCE_TOKENIZER_PATH \
  --sample_number 3 \
  --heading_level 1 \
  --storage_method raw \
  --random_split False \
  --consecutive True 

INPUT_FOLDER is the cleaned dataset from the previous step. In case you want to pre-process the data with sentencepiece tokenizer you need to set the path in sentence_tokenizer_path. 'sample_number' is the number of positive and negative samples, our default is 3. heading_level allows to choose between the first and second level headings in the data, all the models from the paper were trained using the first level heading. storage_method allows for further pre-processing by tokenizing based on bert, roberta or sentence-piece tokenizer. Most models in the paper were using the raw format. If random_split is applied then all the paragraph and sections are mixed and then randomly shuffled for the test and train set. If consecutive is set then the positive examples come from the paragraphs belonging to the same section. In the paper, we discuss three different training strategies. For Section Prediction, in the previous step the paragraph flag has to be set to False and the random_split set to True, also consecutive set to False. For Random Paragraph, in the previous step the paragraph flag has to be set to True and the random_split set to True, also consecutive set to False. For Consecutive Paragraph, in the previous step the paragraph flag has to be set to True and the random_split set to False, also consecutive set to True.

As for the baselines, we have two separate scripts to transform our data to the valid format for the models, example:

python baselines/ --input_folder INPUT_FOLDER --output_folder OUTPUT_FOLDER
python baselines/ --input_folder INPUT_FOLDER --output_folder OUTPUT_FOLDER --heading_level 1

where the INPUT_FOLDER contains the cleaned data from step one.

Step three (training):

For training we refer to the Training from Scratch section, where the code for each model can be found. We also have some helper script for the GraphSeg algorithm, as it usually crashes when the paragraphs are too small. To resume training you can use:

python baselines/ --existing_dir EXISTING_DIR \
--new_dir NEW_DIR \
--so_far_completed_dir SO_FAR_COMPLETED_DIR

where the EXISTING_DIR contains all the data and NEW_DIR is the path to save the resumed data files, SO_FAR_COMPLETED_DIR contains the files that have already been processed.

Step four and five (evaluation):

The prediction accuracy for Same Topic Prediction task is reported during training and final evaluation according to the scripts available in the forked repositories. For the text segmentation task, the code to generate the plots and results reported in the paper is in our_model/ To run the code successfully you need the trained models from the transformed based, sentence-transformer and all the baselines. For GraphSeg and WikiSeg, we had to modify their output to be comparable to our results. GraphSeg's default output format already indicates the position of separations, we use baselines/ to convert this result to a comparable paragraph-based prediction. The same applies to textseg, baselines/ provides code for loading a trained model and making segment prediction based on it. This script operates in the textseg environment which is different form the rest of the repository. The output of this script has to be further processed by baselines/ to achieve comparable results.

Example of the back translation:

python baselines/ --input_folder INPUT_FOLDER \
--seg_folder SEG_FOLDER \
--output_folder OUTPUT_FOLDER \
--method {textseg,graphseg}

where the method parameter defines whether to use WikiSeg (sometimes referred to as textseg) or GraphSeg, SEG_FOLDER must contain the segmented text from the models and the INPUT_FOLDER the original test data.


If you use our code, dataset, or model weights in your research, please cite.




Code accompanying the submission "Structural Text Segmentation of Legal Documents" by Aumiller et al.







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