C2F-FAR

This is the code for COLING 2022 paper: An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks

Update 2022-08-31: Simple code without tidying up.

TODO: I will refactor the code later when I have time.

We resplit the sentence of billsum with stanfordcorenlp v4.5.1 and place the processed file in data/billsum/test.json.

requirements

• python == 3.7.0
• pip install -r requirements.txt

Runing from the scratch

The YAML format config files of all steps are placed in yaml_config/.

Step 1: Obtain the sentence level document embeddings

Firstly, you should modify the config in yaml_config/encode_document.yaml.

• model_name_or_path: the downloaded pre-trained bert model or sbert model.
• batch_size: the total batch size (we encode the document on 4xV100 32G, if you use different device, you should modify it.)

Then, you can run it by:

python src/encode_document.py yaml_config/encode_document.yaml

After that, you can see the test.cls.[model_name].[max_length].pt file in the folder tmp_file/billsum/sentence_embeddings/.

Step 2: Split the document into Semantic Blocks

You can split the document into Semantic Blocks by running:

python src/split_document.py

After that, you can see the test.cls.[model_name].[max_length].[beta].pt file in the folder tmp_file/billsum/partitions/.

If you want to change the model, dataset and so on, you can change them in the line [84-89]:

dataset = "billsum"
beta = 1
data_path = "tmp_file/"
model_name = "sbert"
# model_name = "bert-base-uncased"
max_length = 80

Step 3: Rank and Extract the summary sentences

Firstly, you should modify the config in yaml_config/ranker.yaml.

• model_path: just given sbert or bert-base-uncased.

Then rank and extract the summary sentences by:

python src/ranker.py

After that, you can see the result file sbert.cls.dot-product.tokens_200.f_0.b_0.0.l1_0.5.l2_0.5.txt file in the folder tmp_file/billsum/c2f_results/.

Step 4: Evaluate the results

Finally, you can run python src/metric.py [results_file_path] to get the results of c2f-far.

Download the model and embeddings

You can download results, embeddings, and pretrained_models and then place it directly in the c2f-far/.

• Google Drive: https://drive.google.com/file/d/1odDgbhXiAYFB7bZxRD-FxvzXHwdTGqGz/view?usp=sharing
• Baidu Cloud: https://pan.baidu.com/s/1PFgH9CQA3DiETWu4Ua-9yw code: fdik