A Python wrapper for the ROUGE summarization evaluation package.
pyrouge is a Python wrapper for the ROUGE summarization evaluation package. Getting ROUGE to work can require quite a bit of time. pyrouge is designed to make getting ROUGE scores easier by automatically converting your summaries into a format ROUGE understands, and automatically generating the ROUGE configuration file.
I have summarizations in plain text format and want to get the ROUGE scores
You can evaluate your plain text summaries like this:
from pyrouge import Rouge155 r = Rouge155() r.system_dir = 'path/to/system_summaries' r.model_dir = 'path/to/model_summaries' r.system_filename_pattern = 'some_name.(\d+).txt' r.model_filename_pattern = 'some_name.[A-Z].#ID#.txt' output = r.convert_and_evaluate() print(output) output_dict = r.output_to_dict(output)
In order to evaluate summaries, ROUGE needs to know where your summaries and the gold standard summaries are, and how to match them. In ROUGE parlance, your summaries are ‘system’ summaries and the gold standard summaries are ‘model’ summaries. The summaries should be in separate folders, whose paths are set with the system_dir and model_dir variables. All summaries should contain one sentence per line.
To automatically match a system summary with the corresponding model summaries, pyrouge uses regular expressions. For example, let’s assume your system summaries are named with a combination of a fixed name and a variable numeric ID like this:
and the model summaries like this, with uppercase letters identifying multiple model summaries for a given document:
The group in the system_filename_pattern tells pyrouge which part of the filename is the ID – in this case (\d+). You have to use round brackets to indicate a group, or else pyrouge won’t be able to tell apart the ID from the rest of the filename. pyrouge then uses that ID to find all matching model summaries. The special placeholder #ID# tells pyrouge where it should expect the ID in the model_filename_pattern. The [A-Z] part matches multiple model summaries for that ID.
With the configuration done, invoking convert_and_evaluate() gets you the ROUGE scores as a string. If you want to further process the scores, you can parse the output into a dict with output_to_dict(output).
I only want to preprocess my summaries and then run ROUGE on my own
To convert plain text summaries into a format ROUGE understands, do:
from pyrouge import Rouge155 Rouge155.convert_summaries_to_rouge_format(system_input_dir, system_output_dir) Rouge155.convert_summaries_to_rouge_format(model_input_dir, model_output_dir)
This will convert all summaries in system_input_dir and model_input_dir, and save them to their respective output directories.
To generate the configuration file that ROUGE uses to match system and model summaries, do:
from pyrouge import Rouge155 Rouge155.write_config_static( system_dir, system_filename_pattern, model_dir, model_filename_pattern, config_file_path)
The first four arguments are explained above. config_file_path specifies where to save the configuration file.
Using pyrouge from the command line
If you prefer the command line to Python and the pyrouge module, you can use the following scripts, which are automatically installed and should be runnable from anywhere on your system:
pyrouge_evaluate_plain_text_files gets you ROUGE scores for your plain text summaries. Example:
pyrouge_evaluate_plain_text_files -s systems_plain/ -sfp "some_name.(\d+).txt" -m models_plain/ -mfp some_name.[A-Z].#ID#.txt
pyrouge_evaluate_rouge_format_files gets you ROUGE scores for summaries already converted to ROUGE format. Example usage for the sample-test/SL2003 data that comes with ROUGE:
pyrouge_evaluate_rouge_format_files -s systems -sfp "SL.P.10.R.11.SL062003-(\d+).html" -m models -mfp SL.P.10.R.[A-Z].SL062003-#ID#.html
Note that the system filename pattern is enclosed in quotation marks because it contains special characters.
pyrouge_convert_plain_text_to_rouge_format converts plain text files into a format ROUGE understands. If your plain text files do not contain one sentence per line, this script can also split sentences, provided you have nltk and its Punkt sentence splitter installed. Example:
pyrouge_convert_plain_text_to_rouge_format -i models_plain/ -o models_rouge
pyrouge_write_config_file creates a configuration file you can use to run ROUGE on your own. Example:
pyrouge_write_config_file -s systems -sfp "SL.P.10.R.11.SL062003-(\d+).html" -m models -mfp SL.P.10.R.[A-Z].SL062003-#ID#.html -c sl2003_config.xml
Running any of these with the -h option will display a usage message explaining the various command line options.
Instruction on installing ROUGE can be found here.
Depending on your system, you might have to run the following commands as root.
To install pyrouge, run:
pip install pyrouge
Assuming a working ROUGE-1.5.5. installation, tell pyrouge the ROUGE path with this command:
To test if everything is installed correctly, run:
python -m pyrouge.test
If everything works, you should see something like:
Ran 10 tests in 18.055s OK
If you want to uninstall pyrouge:
pip uninstall pyrouge