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README.md

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+<div align="center">
+<h1> SummAct: Uncovering User Intentions Through Interactive Behaviour Summarisation </h1>
+    
+**[Guanhua Zhang][4], &nbsp; [Mohamed Ahmed][3], &nbsp; [Zhiming Hu][5], &nbsp; [Andreas Bulling][6]** <br>
+**ACM CHI 2025**, Yokohama, Japan <br>
+**[[Project][2]]** **[[Paper][7]]** </div>
+----------------
+
+# Directory Structure
+```
+SummAct
+│   README.md
+│   environment.yml
+│
+└───preprocess
+│   convert_dataset.py
+│   create_steps.py
+│
+└───hf_bmt
+│   hf_2_bmtrain.py
+│   hf_2_bmtrain.sh
+│   bmt_hf.py
+│
+└───train
+│   train.py
+│   train.sh
+│
+└───inference
+│   inference.py
+│   inference.sh
+│
+└───train
+│   train.py
+│   train.sh
+│
+└───inference
+│   inference.py
+│   inference.sh   
+
+```
+# Setup
+We recommend setting up a virtual environment using Anaconda. <br>
+1. Create a conda environment and install dependencies
+   ```shell
+   conda env create --name summact --file=env.yaml
+   conda activate summact
+    ```
+2. Since `model_center==1.0.3` is needed but is not yet available on PYPI, build from [source](https://github.com/OpenBMB/ModelCenter)
+    ```
+    $ git clone https://github.com/OpenBMB/ModelCenter.git
+    $ cd ModelCenter
+    $ pip install -r requirements.txt
+    $ python3 setup.py install
+    ```
+3. Clone our repository to download our code and a pretrained model
+   ```shell
+   git clone this_repo.git
+   ```
+
+# Preprocessing
+1. Convert actions from symbolic formats to natural language by running `preprocess/convert_dataset.py`. Adapt it to your local dataset paths.
+2. Prompting the pretrained LLM with examples to generate sub-intentions using `preprocess/create_steps.py`. Adapt it to your local prompt txt path.
+
+# Fine-tuning
+1. After downloading the model from Hugging Face, convert it into `model_center` weights using the script in `hf_bmt/hf_2_bmtrain.sh`. Adapt it to your local paths of the downloaded model and the wanted output.
+2. Run `train/train.sh`, which will call `train/train.py` to fine-tune the model for interactive behaviour summarisation. Make sure your computer has GPUs.
+
+# Inference
+Run `inference/inference.sh`, which will call `inference/inference.py` to convert the fine-tuned model back to HF format, and then calculate metrics to evaluate the summarisation quality.
+
+# Citation 
+If you find our code useful or use it in your own projects, please cite our paper:
+```
+@inproceedings{zhang25_chi,
+  title = {SummAct: Uncovering User Intentions Through Interactive Behaviour Summarisation},
+  author = {Zhang, Guanhua and Ahmed, Mohamed and Hu, Zhiming and Bulling, Andreas},
+  year = {2025},
+  pages = {1--17},
+  booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)},
+  doi = {10.1145/3706598.3713190}
+}
+```
+
+# Acknowledgements
+Our work relied on the codebase of [Mind2Web][1], [ScreenAgent][8] and [Tell Me More!][9]. Thanks to the authors for sharing their code.
+
+[1]: https://osu-nlp-group.github.io/Mind2Web/
+[2]: https://collaborative-ai.org/publications/zhang25_chi/
+[3]: https://www.linkedin.com/in/mohamed-adel-naguib/
+[4]: https://scholar.google.com/citations?user=NqkK0GwAAAAJ&hl=en
+[5]: https://scholar.google.com/citations?hl=en&user=OLB_xBEAAAAJ
+[6]: https://www.collaborative-ai.org/people/bulling/
+[7]: https://collaborative-ai.org/publications/zhang25_chi.pdf
+[8]: https://github.com/niuzaisheng/ScreenAgent
+[9]: https://github.com/OpenBMB/Tell_Me_More