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import datasets |
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import pdb |
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import jsonlines |
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CITATION_BLOB = ''' |
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@article{krishna2023usb, |
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title={USB: A Unified Summarization Benchmark Across Tasks and Domains}, |
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author={Krishna, Kundan and Gupta, Prakhar and Ramprasad, Sanjana and Wallace, Byron C and Bigham, Jeffrey P and Lipton, Zachary C}, |
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booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023}, |
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year={2023} |
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} |
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''' |
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DESCRIPTION_BLOB = ''' |
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The USB benchmark consists of labeled datasets for a collection of 8 tasks dealing with text summarization, |
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particularly focusing on factuality and controllability of summary generation. |
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Paper can be found here : https://arxiv.org/abs/2305.14296 |
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''' |
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class USBConfig(datasets.BuilderConfig): |
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def __init__( |
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self, |
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text_features, |
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label_column, |
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citation=CITATION_BLOB, |
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data_url="processed_data.tar.gz", |
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label_classes=None, |
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process_label=lambda x: x, |
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**kwargs, |
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): |
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super(USBConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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self.text_features = text_features |
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self.label_column = label_column |
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self.citation = citation |
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self.label_classes = label_classes |
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self.process_label = process_label |
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self.url = "https://github.com/kukrishna/usb" |
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self.data_url=data_url |
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class USB(datasets.GeneratorBasedBuilder): |
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"""The Unified Summarization Benchmark.""" |
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BUILDER_CONFIGS = [ |
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USBConfig( |
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name="topicbased_summarization", |
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description="Generate a short summary of the given article covering the given topic", |
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text_features={"summ_idx": "int", "input_lines": "listsent", "topic_name": "sent", "output_lines":"listsent"}, |
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label_column="output_lines", |
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), |
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USBConfig( |
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name="fixing_factuality", |
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description="Given a summary sentence (claim) and presented evidence from the article, edit the summary to remove unsupported or contradicting facts", |
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text_features={"summ_idx": "int", "input_lines": "listsent", "initial_summary": "sent", "fixed_summary":"sent"}, |
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label_column="fixed_summary", |
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), |
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USBConfig( |
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name="unsupported_span_prediction", |
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description="Given a summary sentence (claim) and presented evidence from the article, mark the parts of the summary which are not supported by the evidence by surrounding them with [] and [/] tags.", |
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text_features={"summ_idx": "int", "input_lines": "listsent", "summary": "sent", "annotated_summary":"sent"}, |
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label_column="annotated_summary", |
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), |
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USBConfig( |
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name="evidence_extraction", |
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description="Given an article and its summary, for each summary sentence, produce a minimal list of sentences from the article which provide sufficient evidence for all facts in the summary sentence.", |
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text_features={"input_lines": "listsent", "summary_lines": "listsent", "evidence_labels":"listlistint"}, |
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label_column="evidence_labels", |
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), |
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USBConfig( |
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name="multisentence_compression", |
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description="Given a list of sentences from an article, generate a single sentence summary of the presented cluster of sentences.", |
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text_features={"summ_idx": "int", "input_lines": "listsent", "output_lines": "listsent"}, |
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label_column="output_lines", |
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), |
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USBConfig( |
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name="extractive_summarization", |
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description="Given an article, generate an extractive summary by producing a subset o the article's sentences", |
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text_features={"input_lines": "listsent", "labels": "listint"}, |
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label_column="labels", |
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), |
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USBConfig( |
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name="abstractive_summarization", |
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description="Given an article, generate its abstractive summary", |
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text_features={"input_lines": "listsent", "output_lines": "listsent"}, |
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label_column="output_lines", |
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), |
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USBConfig( |
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name="factuality_classification", |
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description="Given a summary sentence (claim) and presented evidence from the article, predict whether all facts of the claim are supported by and in agreement with the presented evidence, or not.", |
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text_features={"summ_idx": "int", "input_lines": "listsent", "summary_sent": "sent", "label":"int"}, |
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label_column="label", |
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), |
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] |
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def _split_generators(self, dl_manager): |
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data_root = dl_manager.download_and_extract(self.config.data_url) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"data_file": f"{data_root}/{self.config.name}/train.jsonl", |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"data_file": f"{data_root}/{self.config.name}/validation.jsonl", |
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"split": "validation", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"data_file": f"{data_root}/{self.config.name}/test.jsonl", |
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"split": "test", |
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}, |
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), |
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] |
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def _generate_examples(self, data_file, split): |
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with jsonlines.open(data_file) as f: |
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for ex_idx,example in enumerate(f): |
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example["id"] = example["id"]+":"+str(ex_idx) |
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example["domain"] = example["id"].split("/")[0] |
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yield example["id"], example |
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def _info(self): |
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features = {} |
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features["id"] = datasets.Value("string") |
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features["domain"] = datasets.Value("string") |
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for (text_feature,dtype) in self.config.text_features.items(): |
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hf_dtype = None |
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if dtype=="int": |
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hf_dtype = datasets.Value("int32") |
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elif dtype=="listint": |
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hf_dtype = datasets.Sequence(datasets.Value("int32")) |
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elif dtype=="listlistint": |
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hf_dtype = datasets.Sequence(datasets.Sequence(datasets.Value("int32"))) |
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elif dtype=="sent": |
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hf_dtype = datasets.Value("string") |
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elif dtype=="listsent": |
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hf_dtype = datasets.Sequence(datasets.Value("string")) |
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else: |
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raise NotImplementedError |
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features[text_feature] = hf_dtype |
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return datasets.DatasetInfo( |
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description=DESCRIPTION_BLOB, |
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features=datasets.Features(features), |
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homepage=self.config.url, |
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citation=self.config.citation, |
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) |
