diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c9ee488 --- /dev/null +++ b/.gitignore @@ -0,0 +1,7 @@ +# python cache +__pycache__ + +# Downloaded models and data +meteor-1.5.jar +stanford-corenlp-full-2018-10-05 +deps.words \ No newline at end of file diff --git a/README.md b/README.md index 4b54cd7..4a40209 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,200 @@ # Summarization Repository -Authors: [Alex Fabbri*](http://alex-fabbri.github.io/), [Wojciech Kryściński*](https://twitter.com/iam_wkr), [Bryan McCann](https://bmccann.github.io/), [Caiming Xiong](http://cmxiong.com/), [Richard Socher](https://www.socher.org/), and [Dragomir Radev](http://www.cs.yale.edu/homes/radev/)
+This repo was initially released with the [SummEval](#citation) paper. It +contains the python package [`summ-eval`](https://pypi.org/project/summ-eval/), +a toolkit for summarization evaluation, as well as the [dataset](#dataset) used +in the original publication. -This project is a collaboration work between [Yale LILY Lab](https://yale-lily.github.io/) and [Salesforce Research](https://einstein.ai/).

-

-LILY Logo -            -Salesforce Logo -

+## Table of Contents -\* - Equal contributions from authors +1. [Evaluation Toolkit](#evaluation-toolkit) +2. [Dataset](#dataset) +3. [Contribute](#contribute) +4. [Updates](#updates) +5. [Citation](#citation) -## Table of Contents -1. [Updates](#updates) -2. [Data](#data) -3. [Evaluation Toolkit](#evaluation-toolkit) -4. [Citation](#citation) -5. [Get Involved](#get-involved) +## `summ-eval`: An Evaluation Toolkit -## Updates -_04/19/2020_ - Updated the [human annotation file](https://drive.google.com/file/d/1d2Iaz3jNraURP1i7CfTqPIj8REZMJ3tS/view?usp=sharing) to include all models from paper and metric scores.
-_04/19/2020_ - SummEval is now pip-installable! Check out the [pypi page](https://pypi.org/project/summ-eval/).
-_04/09/2020_ - Please see [this comment](https://github.com/Yale-LILY/SummEval/issues/13#issuecomment-812918298) with code for computing system-level metric correlations!
-_11/12/2020_ - Added the reference-less BLANC and SUPERT metrics!
-_7/16/2020_ - Initial commit! :) +This toolkit for summarization evaluation is provided to unify metrics and promote robust comparison of summarization +systems. The toolkit contains popular and recent metrics for summarization as well as several machine translation metrics. +We invite contributions of additional metrics, please [contribute](#contribute). + +The metrics currently included in the tookit, with the associated paper and code used within the toolkit, and where they +can be found within the package. +|Metric|Paper|Code| +|-|-|-| +|ROUGE|[ROUGE: A Package for Automatic Evaluation of Summaries](https://www.aclweb.org/anthology/W04-1013.pdf)|[Link](https://github.com/bheinzerling/pyrouge/tree/master/pyrouge)| +|ROUGE-we|[Better Summarization Evaluation with Word Embeddings for ROUGE](https://www.aclweb.org/anthology/D15-1222.pdf)|[Link](https://github.com/UKPLab/emnlp-ws-2017-s3/blob/master/S3/ROUGE.py#L152)| +|MoverScore|[MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://www.aclweb.org/anthology/D19-1053.pdf)|[Link](https://github.com/AIPHES/emnlp19-moverscore/)| +|BertScore|[BertScore: Evaluating Text Generation with BERT](https://arxiv.org/pdf/1904.09675.pdf)|[Link](https://github.com/Tiiiger/bert_score)| +|Sentence Mover's Similarity|[Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts](https://www.aclweb.org/anthology/P19-1264.pdf)|[Link](https://github.com/eaclark07/sms)| +|SummaQA|[Answers Unite! Unsupervised Metrics for Reinforced Summarization Models](https://www.aclweb.org/anthology/D19-1320.pdf)|[Link](https://github.com/recitalAI/summa-qa)| +|BLANC|[Fill in the BLANC: Human-free quality estimation of document summaries](https://arxiv.org/pdf/2002.09836.pdf)|[Link](https://github.com/PrimerAI/blanc)| +|SUPERT|[SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.124.pdf)|[Link](https://github.com/yg211/acl20-ref-free-eval)| +|METEOR|[METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments ](https://www.aclweb.org/anthology/W05-0909.pdf)|[Link](https://github.com/Maluuba/nlg-eval/tree/master/nlgeval/pycocoevalcap/meteor)| +|S3|[Learning to Score System Summaries for Better Content Selection Evaluation](https://www.aclweb.org/anthology/W17-4510/)|[Link](https://github.com/UKPLab/emnlp-ws-2017-s3)| +|Misc. statistics
(extractiveness, novel n-grams, repetition, length)|[Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://www.aclweb.org/anthology/N18-1065/)| [Link](https://github.com/lil-lab/newsroom)| +|Syntactic Evaluation|[Automatic Analysis of Syntactic Complexity in Second Language writing](https://www.benjamins.com/catalog/ijcl.15.4.02lu)|[Link](http://www.personal.psu.edu/xxl13/downloads/L2SCA-2016-06-30.tgz)| +|CIDer|[CIDEr: Consensus-based Image Description Evaluation](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vedantam_CIDEr_Consensus-Based_Image_2015_CVPR_paper.pdf)|[Link](https://github.com/Maluuba/nlg-eval/tree/master/nlgeval/pycocoevalcap/cider)| +|CHRF|[CHRF++: words helping character n-grams](https://www.statmt.org/wmt17/pdf/WMT70.pdf)|[Link](https://github.com/m-popovic/chrF)| +|BLEU|[BLEU: a Method for Automatic Evaluation of Machine Translation](https://www.aclweb.org/anthology/P02-1040.pdf)|[Link](https://github.com/mjpost/sacreBLEU)| + + +## Setup + +You can install the base package `summ_eval` via pip: +```bash +pip install summ-eval +``` + +You can also install summ_eval from source: + +``` +git clone https://github.com/Yale-LILY/SummEval.git +cd evaluation +pip install -e . +``` + +Additional setup is required for some metrics. Many of these should run when first +imported. If you run into issues, please create an issue on this repository. + +### Rouge + +The original Perl rouge implementation is used to ensure consistency with previous work. +This requires the perl scripts to be downloaded from this repository and the location +of the download to be set as `ROUGE_HOME`. These steps should happen when you +first import the `rouge_metric` sub-module. + +Additionally, the XML:Parser Perl package is required. This can be installed with +```bash +apt-get install libxml-parser-perl +``` +in ubuntu systems, at least. + +Finally, the `pyrouge` python package is required. This can be installed manually +or with the `summ-eval[rouge]` extra. + + +### S3; Sentence Mover's Similarity and Supert +These metrics all require NLTK's `stopwords` corpus. This can be installed with +```bash +python -m nltk.downloader stopwords +``` + +### Supert +Supert requires NLTK's `punkt` tokenizer. This can be installed with +```bash +python -m nltk.downloader tokenizer +``` + +Additionally, this metric the `sentence_transformers` folder to be findable by importlib. +To do this, `./evaluation/summ_eval/` must be added to the python path. +This can be done either manually with `export PYTHONPATH=$PYTHONPATH:./evaluation/summ_eval/` or +will be done automatically when `summ_eval.supert_metric` is imported. + +### Meteor +This implementation is in java so requires the java run time environment. +This can be installed with +```bash +apt-get install default-jre +``` + +### Blanc; Mover score +As currently implemented, these metrics require CUDA acceleration. +This will require cuda to be installed, which can be confirmed by successfully running `nvidia-smi` and having +the appropriate GPU reported. + +Installation instructions are provided by [NVidia](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html). + +## Usage -## Data +### As a package +Each metric can be imported and instantiated as a class. + +```python +from summ_eval.selected_metric import SelectedMetric +metric = SelectedMetric() +``` + +With the metric instantiated, you can evaluate a batch of summaries and references, or a single example. +```python +batch_summaries = ["This is one summary", "This is another summary"] +batch_references = ["This is one reference", "This is another"] + +batch_result_dict = metric.evaluate_batch(batch_summaries, batch_references) +single_result_dict = metric.evaluate_example(summaries[0], references[0]) +``` + + +Each metric has a `supports_multi_ref` property to indicate whether it supports multiple references. +If it does, then a list of references can be passed to `evaluate` and `evaluate_batch` for each +summary. + +```python +assert metric.supports_multi_ref +multi_references = [ + ["This is ref 1 for summ 1", "This is ref 2 for summ 1"], + ["This is ref 1 for summ 2", "This is ref 2 for summ 2"] +] +batch_result_dict = metric.evaluate_batch(batch_summaries, multi_references) +single_result_dict = metric.evaluate_example(summaries[0], multi_references[0]) +``` + +### As a CLI +If you want to use the evaluation metrics from a shell, we have you covered! +We provide a command-line interface, `calc-scores`. Use +```bash +calc-scores --help +``` +for a full list of options. + +TLDR: you will need to specify +* `--summ-file`: a file containing summaries, one per line +* `--ref-file`: a file containing references, one per line +* `--output-file`: a file to write the results to +* `--metrics`: a comma-separated list of metrics to use + +For example, to run the ROUGE metric and write the results to `rouge.jsonl`, analogous to [files2rouge](https://github.com/pltrdy/files2rouge). +```bash +calc-scores --config-file=examples/basic.config --metrics "rouge" --summ-file summ_eval/1.summ --ref-file summ_eval/1.ref --output-file rouge.jsonl --eos " . " --aggregate True +``` + +Other options exist for file I/O convenience as well as configuration. + +To run ROUGE and BertScore on a `.jsonl` file which contains `reference` and `decoded` keys (i.e., system output) and write to `output.jsonl`. +```bash +calc-scores --config-file=examples/basic.config --metrics "rouge, bert_score" --jsonl-file data.jsonl --output-file rouge_bertscore.jsonl +``` + +The CLI makes use of [gin config](https://github.com/google/gin-config) files to more conveniently set metric parameters. + +**NOTE**: if you're seeing slow-ish startup time, try commenting out the metrics you're not using in the config; otherwise this will load all modules. + +**NOTE** currently the command-line tool does not support multiple references for simplicity of the interface. This will +have to be completed with a python script. + + +### Metric naming +The metrics can be imported from +|Metric|Import|CLI string +|-|-|-| +|ROUGE|`from summ_eval.rouge_metric import RougeMetric`|`rouge`| +|ROUGE-we|`from summ_eval.rouge_metric import RougeWeMetric`|`rouge_we`| +|MoverScore|`from summ_eval.mover_score import MoverScoreMetric`|`mover_score`| +|BertScore|`from summ_eval.bert_score import BertScoreMetric`|`bert_score`| +|Sentence Mover's Similarity|`from summ_eval.sms_metric import SmsMetric`|`sms`| +|SummaQA|`from summ_eval.summa_qa_metric import SummaQaMetric`|`summa_qa`| +|BLANC|`from summ_eval.blanc_metric import BlancMetric`|`blanc`| +|SUPERT|`from summ_eval.supert_metric import SupertMetric`|`supert`| +|METEOR|`from summ_eval.meteor_metric import MeteorMetric`|`meteor`| +|S3|`from summ_eval.s3_metric import S3Metric`|`s3`| +|Misc. statistics
(extractiveness, novel n-grams, repetition, length)|`from summ_eval.misc_stats import MiscStatsMetric`|`misc_stats`| +|Syntactic Evaluation|`from summ_eval.syntactic_metric import SyntacticMetric`|`syntactic`| +|CIDer|`from summ_eval.cider_metric import CiderMetric`|`cider`| +|CHRF|`from summ_eval.chrf_metric import ChrfMetric`|`chrf`| +|BLEU|`from summ_eval.bleu_metric import BleuMetric`|`bleu`| +## SummEval: a Dataset As part of this release, we share summaries generated by recent summarization model trained on the CNN/DailyMail dataset [here](#model-outputs).
We also share human annotations, collected from both crowdsource workers and experts [here](#human-annotations). @@ -96,112 +264,48 @@ Example call for _human annotations_: `python3 data_processing/pair_data.py --data_annotations --story_files ` -## Evaluation Toolkit -We provide a toolkit for summarization evaluation to unify metrics and promote robust comparison of summarization systems. The toolkit contains popular and recent metrics for summarization as well as several machine translation metrics. - -### Metrics ### -Below are the metrics included in the tookit, followed by the associated paper and code used within the toolkit: -|Metric|Paper|Code| -|-|-|-| -|ROUGE|[ROUGE: A Package for Automatic Evaluation of Summaries](https://www.aclweb.org/anthology/W04-1013.pdf)|[Link](https://github.com/bheinzerling/pyrouge/tree/master/pyrouge)| -|ROUGE-we|[Better Summarization Evaluation with Word Embeddings for ROUGE](https://www.aclweb.org/anthology/D15-1222.pdf)|[Link](https://github.com/UKPLab/emnlp-ws-2017-s3/blob/master/S3/ROUGE.py#L152)| -|MoverScore|[MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://www.aclweb.org/anthology/D19-1053.pdf)|[Link](https://github.com/AIPHES/emnlp19-moverscore/)| -|BertScore|[BertScore: Evaluating Text Generation with BERT](https://arxiv.org/pdf/1904.09675.pdf)|[Link](https://github.com/Tiiiger/bert_score)| -|Sentence Mover's Similarity|[Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts](https://www.aclweb.org/anthology/P19-1264.pdf)|[Link](https://github.com/eaclark07/sms)| -|SummaQA|[Answers Unite! Unsupervised Metrics for Reinforced Summarization Models](https://www.aclweb.org/anthology/D19-1320.pdf)|[Link](https://github.com/recitalAI/summa-qa)| -|BLANC|[Fill in the BLANC: Human-free quality estimation of document summaries](https://arxiv.org/pdf/2002.09836.pdf)|[Link](https://github.com/PrimerAI/blanc)| -|SUPERT|[SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.124.pdf)|[Link](https://github.com/yg211/acl20-ref-free-eval)| -|METEOR|[METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments ](https://www.aclweb.org/anthology/W05-0909.pdf)|[Link](https://github.com/Maluuba/nlg-eval/tree/master/nlgeval/pycocoevalcap/meteor)| -|S3|[Learning to Score System Summaries for Better Content Selection Evaluation](https://www.aclweb.org/anthology/W17-4510/)|[Link](https://github.com/UKPLab/emnlp-ws-2017-s3)| -|Misc. statistics
(extractiveness, novel n-grams, repetition, length)|[Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://www.aclweb.org/anthology/N18-1065/)| [Link](https://github.com/lil-lab/newsroom)| -|Syntactic Evaluation|[Automatic Analysis of Syntactic Complexity in Second Language writing](https://www.benjamins.com/catalog/ijcl.15.4.02lu)|[Link](http://www.personal.psu.edu/xxl13/downloads/L2SCA-2016-06-30.tgz)| -|CIDer|[CIDEr: Consensus-based Image Description Evaluation](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vedantam_CIDEr_Consensus-Based_Image_2015_CVPR_paper.pdf)|[Link](https://github.com/Maluuba/nlg-eval/tree/master/nlgeval/pycocoevalcap/cider)| -|CHRF|[CHRF++: words helping character n-grams](https://www.statmt.org/wmt17/pdf/WMT70.pdf)|[Link](https://github.com/m-popovic/chrF)| -|BLEU|[BLEU: a Method for Automatic Evaluation of Machine Translation](https://www.aclweb.org/anthology/P02-1040.pdf)|[Link](https://github.com/mjpost/sacreBLEU)| +## Contribute +Please create a GitHub issue if you have any questions, suggestions, requests or bug-reports. +We welcome PRs! -#### SETUP #### -You can install summ_eval via pip: +### Setup +The project uses [poetry](https://python-poetry.org/) for dependency management so +after installing poetry, installing the project dependencies is as simple as ```bash -pip install summ-eval +poetry install --all-extras ``` -You can also install summ_eval from source: - -``` -git clone https://github.com/Yale-LILY/SummEval.git -cd evaluation -pip install -e . -``` +### Testing You can test your installation (assuming you're in the `./summ_eval` folder) and get familiar with the library through `tests/` ``` -python -m unittest discover -``` - - -### Command-line interface -We provide a command-line interface `calc-scores` which makes use of [gin config](https://github.com/google/gin-config) files to set metric parameters. - -##### Examples -Run ROUGE on given source and target files and write to `rouge.jsonl`, analogous to [files2rouge](https://github.com/pltrdy/files2rouge). -``` -calc-scores --config-file=examples/basic.config --metrics "rouge" --summ-file summ_eval/1.summ --ref-file summ_eval/1.ref --output-file rouge.jsonl --eos " . " --aggregate True -``` - -**NOTE**: if you're seeing slow-ish startup time, try commenting out the metrics you're not using in the config; otherwise this will load all modules. - - -Run ROUGE and BertScore on a `.jsonl` file which contains `reference` and `decoded` (i.e., system output) keys and write to `output.jsonl`. -``` -calc-scores --config-file=examples/basic.config --metrics "rouge, bert_score" --jsonl-file data.jsonl --output-file rouge_bertscore.jsonl -``` - -For a full list of options, please run: -``` -calc-scores --help -``` - - -### For use in scripts -If you want to use the evaluation metrics as part of other scripts, we have you covered! - -``` -from summ_eval.rouge_metric import RougeMetric -rouge = RougeMetric() -``` - -#### Evaluate on a batch -``` -summaries = ["This is one summary", "This is another summary"] -references = ["This is one reference", "This is another"] - -rouge_dict = rouge.evaluate_batch(summaries, references) -``` - -#### Evaluate on a single example -``` -rouge_dict = rouge.evaluate_example(summaries[0], references[0]) -``` - - -#### Evaluate with multiple references -Currently the command-line tool does not use multiple references for simplicity. Each metric has a `supports_multi_ref` property to tell you if it supports multiple references. - -``` -print(rouge.supports_multi_ref) # True -multi_references = [["This is ref 1 for summ 1", "This is ref 2 for summ 1"], ["This is ref 1 for summ 2", "This is ref 2 for summ 2"]] -rouge_dict = rouge.evaluate_batch(summaries, multi_references) +poetry run python -m unittest discover ``` +## Updates +_04/19/2020_ - Updated the [human annotation file](https://drive.google.com/file/d/1d2Iaz3jNraURP1i7CfTqPIj8REZMJ3tS/view?usp=sharing) to include all models from paper and metric scores.
+_04/19/2020_ - SummEval is now pip-installable! Check out the [pypi page](https://pypi.org/project/summ-eval/).
+_04/09/2020_ - Please see [this comment](https://github.com/Yale-LILY/SummEval/issues/13#issuecomment-812918298) with code for computing system-level metric correlations!
+_11/12/2020_ - Added the reference-less BLANC and SUPERT metrics!
+_7/16/2020_ - Initial commit! :) +## Citation +[SummEval: Re-evaluating Summarization Evaluation](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00373/100686) +Authors: [Alex Fabbri*](http://alex-fabbri.github.io/), [Wojciech Kryściński*](https://twitter.com/iam_wkr), [Bryan McCann](https://bmccann.github.io/), [Caiming Xiong](http://cmxiong.com/), [Richard Socher](https://www.socher.org/), and [Dragomir Radev](http://www.cs.yale.edu/homes/radev/)
+\* - Equal contributions from authors -## Citation +The SummEval project is a collaboration between [Yale LILY Lab](https://yale-lily.github.io/) and [Salesforce Research](https://einstein.ai/).

+

+LILY Logo +            +Salesforce Logo +

``` @article{fabbri2020summeval, @@ -211,8 +315,3 @@ rouge_dict = rouge.evaluate_batch(summaries, multi_references) year={2020} } ``` - -### Get Involved - -Please create a GitHub issue if you have any questions, suggestions, requests or bug-reports. -We welcome PRs! diff --git a/evaluation/poetry.lock b/evaluation/poetry.lock new file mode 100644 index 0000000..3adb0a5 --- /dev/null +++ b/evaluation/poetry.lock @@ -0,0 +1,3003 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.5.0" +description = "Reusable constraint types to use with typing.Annotated" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "annotated_types-0.5.0-py3-none-any.whl", hash = "sha256:58da39888f92c276ad970249761ebea80ba544b77acddaa1a4d6cf78287d45fd"}, + {file = "annotated_types-0.5.0.tar.gz", hash = "sha256:47cdc3490d9ac1506ce92c7aaa76c579dc3509ff11e098fc867e5130ab7be802"}, +] + +[[package]] +name = "bert-score" +version = "0.3.13" +description = "PyTorch implementation of BERT score" +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "bert_score-0.3.13-py3-none-any.whl", hash = "sha256:bbbb4c7fcdaa46d7681aff49f37f96faa09ed74e1b150e659bdc6b58a66989b9"}, + {file = "bert_score-0.3.13.tar.gz", hash = "sha256:8ffe5838eac8cdd988b8b1a896af7f49071188c8c011a1ed160d71a9899a2ba4"}, +] + +[package.dependencies] +matplotlib = "*" +numpy = "*" +packaging = ">=20.9" +pandas = ">=1.0.1" +requests = "*" +torch = ">=1.0.0" +tqdm = ">=4.31.1" +transformers = ">=3.0.0" + +[[package]] +name = "blanc" +version = "0.3.3" +description = "Human-free quality estimation of document summaries" +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "blanc-0.3.3-py3-none-any.whl", hash = "sha256:7c968bdfaf8ec69bb6b40ea3410fe90b3dc4060c5251263176418a4b6b1a408c"}, + {file = "blanc-0.3.3.tar.gz", hash = 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b/evaluation/pyproject.toml @@ -0,0 +1,59 @@ +[tool.poetry] +name = "summ-eval" +version = "0.893" +description = "Toolkit for summarization evaluation" +authors = ["Alex Fabbri , Wojciech Kryściński "] +license = "MIT" +readme = "../README.md" +packages = [{include = "summ_eval"}] + + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +pytest = "^7.4.0" + + +[tool.project.urls] +home = "https://github.com/Yale-LILY/SummEval" +repo = "https://github.com/Yale-LILY/SummEval" + +[tool.poetry.dependencies] +python = ">=3.9,<3.13" +bert-score = "^0.3.13" +gin-config = "^0.5.0" +moverscore = "^1.0.3" +pytorch-pretrained-bert = "^0.6.2" +psutil = "^5.9.5" +six = "^1.16.0" +numpy = ">=1.11.0" +stanza = "^1.5.0" +sacremoses = "^0.0.53" +transformers = ">=2.2.0" +spacy = ">=2.2.0" +sacrebleu = "^2.3.1" +pyemd = "^1.0.0" +click = "^8.1.6" +nltk = "^3.8.1" +cython = "^3.0.0" +scipy = "^1.11.1" +networkx = "^3.1" +blanc = "^0.3.3" +wmd = {git = "https://github.com/src-d/wmd-relax.git"} # Update to >1.3.2 when available +scikit-learn = "^1.3.0" +pyrouge = { git = "https://github.com/bheinzerling/pyrouge.git", optional = true } +# boto3 = "1.28.29" # Uncomment for faster dependency resolution then rerun `poetry lock --no-update` with it commented + + +[tool.poetry.extras] +rouge = [ "pyrouge" ] + + +[tool.poetry.scripts] +calc-scores = 'summ_eval.calc_scores:cli_main' + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/evaluation/setup.py b/evaluation/setup.py deleted file mode 100644 index 2a5a695..0000000 --- a/evaluation/setup.py +++ /dev/null @@ -1,51 +0,0 @@ - -from os import path -from setuptools import setup -from setuptools import find_packages -import urllib.request -import pip -pip.main(['install', 'cython']) - - -f = urllib.request.urlopen("https://raw.githubusercontent.com/Yale-LILY/SummEval/master/README.md") -long_description = f.read().decode("utf-8") - -setup(name='summ_eval', - version='0.892', - description='Toolkit for summarization evaluation', - long_description=long_description, - long_description_content_type='text/markdown', - url='https://github.com/Yale-LILY/SummEval', - author='Alex Fabbri, Wojciech Kryściński', - author_email='alexander.fabbri@yale.edu, wojciech.kryscinski@salesforce.com', - license='MIT', - packages=find_packages(), - include_package_data=True, - zip_safe=False, - install_requires=[ - 'bert-score', - 'gin-config', - 'moverscore', - 'pytorch_pretrained_bert', - 'psutil', - 'six', - 'numpy>=1.11.0', - 'stanza', - 'sacremoses', - 'transformers>=2.2.0', - 'spacy>=2.2.0', - 'sacrebleu', - 'pyemd==0.5.1', - 'click', - 'nltk', - 'cython', - 'scipy', - 'networkx', - 'blanc', - ], - entry_points={ - 'console_scripts': [ - 'calc-scores = summ_eval.calc_scores:cli_main', - ], - } -) diff --git a/evaluation/summ_eval/models/en/svr.pyr_score.en.pkl b/evaluation/summ_eval/models/en/svr.pyr_score.en.pkl index a214f08..d3f4160 100644 Binary files a/evaluation/summ_eval/models/en/svr.pyr_score.en.pkl and b/evaluation/summ_eval/models/en/svr.pyr_score.en.pkl differ diff --git a/evaluation/summ_eval/models/en/svr.responsiveness.en.pkl b/evaluation/summ_eval/models/en/svr.responsiveness.en.pkl index 12f608b..0b4c530 100644 Binary files a/evaluation/summ_eval/models/en/svr.responsiveness.en.pkl and b/evaluation/summ_eval/models/en/svr.responsiveness.en.pkl differ diff --git a/evaluation/summ_eval/supert_metric.py b/evaluation/summ_eval/supert_metric.py index 086edb0..5913dbb 100644 --- a/evaluation/summ_eval/supert_metric.py +++ b/evaluation/summ_eval/supert_metric.py @@ -4,11 +4,22 @@ import os from collections import Counter from nltk.tokenize import sent_tokenize +import logging import gin +import sys from summ_eval.sentence_transformers import SentenceTransformer from summ_eval.metric import Metric -from summ_eval.supert_utils import parse_documents, get_all_token_vecs, build_pseudo_ref, get_sbert_score, get_token_vecs +from summ_eval.supert_utils import ( + parse_documents, + get_all_token_vecs, + build_pseudo_ref, + get_sbert_score, + get_token_vecs, +) + + +logger = logging.getLogger(__name__) try: PYTHONPATH = os.environ['PYTHONPATH'] @@ -18,8 +29,9 @@ dirname = os.path.dirname(__file__) if dirname not in PYTHONPATH: - print(f'Please run the following command and add it to your startup script: \n export PYTHONPATH=$PYTHONPATH:{dirname}') - exit() + # This is a hack to make sure that the sentence_transformers folder can be found for loading models + logger.warning(f"{dirname} needs to be set in the `PYTHONPATH` environment variable. Setting it now.") + sys.path.append(dirname) @gin.configurable class SupertMetric(Metric): diff --git a/evaluation/tests/test_s3_metric.py b/evaluation/tests/test_s3_metric.py new file mode 100644 index 0000000..91f224a --- /dev/null +++ b/evaluation/tests/test_s3_metric.py @@ -0,0 +1,27 @@ +# pylint: disable=C0103 +import os +import unittest +from summ_eval.s3_metric import S3Metric +from summ_eval.test_util import CAND_R, REF_R, CANDS, REFS, EPS + + +class TestScore(unittest.TestCase): + def test_score(self): + metric = S3Metric() + score_dict = metric.evaluate_example(CAND_R, REF_R) + self.assertTrue((score_dict["s3_pyr"] - 0.4402288438243088) < EPS) + self.assertTrue((score_dict["s3_resp"] - 0.5103094504071222) < EPS) + + def test_score_batch(self): + metric = S3Metric() + score_dict = metric.evaluate_batch(CANDS, REFS) + for predicted, (pyr, resp) in zip(score_dict, [ + (1.358148717958252, 1.5925579213409842), + (1.1742432908208689, 1.4061338986807543), + (0.6816419565604588, 0.7101254431464145), + ]): + self.assertTrue((predicted["s3_pyr"] - pyr) < EPS) + self.assertTrue((predicted["s3_resp"] - resp) < EPS) + +if __name__ == '__main__': + unittest.main()