| |
|
|
| import datasets |
| import pdb |
| import jsonlines |
|
|
| CITATION_BLOB = ''' |
| @article{krishna2023usb, |
| title={USB: A Unified Summarization Benchmark Across Tasks and Domains}, |
| author={Krishna, Kundan and Gupta, Prakhar and Ramprasad, Sanjana and Wallace, Byron C and Bigham, Jeffrey P and Lipton, Zachary C}, |
| booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023}, |
| year={2023} |
| } |
| ''' |
|
|
| DESCRIPTION_BLOB = ''' |
| The USB benchmark consists of labeled datasets for a collection of 8 tasks dealing with text summarization, |
| particularly focusing on factuality and controllability of summary generation. |
| Paper can be found here : https://arxiv.org/abs/2305.14296 |
| ''' |
|
|
|
|
| class USBConfig(datasets.BuilderConfig): |
| def __init__( |
| self, |
| featurespec, |
| label_column, |
| citation=CITATION_BLOB, |
| data_url="processed_data.tar.gz", |
| label_classes=None, |
| process_label=lambda x: x, |
| **kwargs, |
| ): |
| super(USBConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
| self.featurespec = featurespec |
| self.label_column = label_column |
| |
| self.citation = citation |
| self.label_classes = label_classes |
| self.process_label = process_label |
| self.url = "https://github.com/kukrishna/usb" |
|
|
| self.data_url=data_url |
|
|
|
|
| class USB(datasets.GeneratorBasedBuilder): |
| """The Unified Summarization Benchmark.""" |
|
|
| BUILDER_CONFIGS = [ |
| USBConfig( |
| name="topicbased_summarization", |
| description="Generate a short summary of the given article covering the given topic", |
| featurespec={"summ_idx": "int", "input_lines": "listsent", "topic_name": "sent", "output_lines":"listsent"}, |
| label_column="output_lines", |
| ), |
| USBConfig( |
| name="fixing_factuality", |
| description="Given a summary sentence (claim) and presented evidence from the article, edit the summary to remove unsupported or contradicting facts", |
| featurespec={"summ_idx": "int", "input_lines": "listsent", "initial_summary": "sent", "fixed_summary":"sent"}, |
| label_column="fixed_summary", |
| ), |
| USBConfig( |
| name="unsupported_span_prediction", |
| 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.", |
| featurespec={"summ_idx": "int", "input_lines": "listsent", "summary": "sent", "annotated_summary":"sent"}, |
| label_column="annotated_summary", |
| ), |
| USBConfig( |
| name="evidence_extraction", |
| 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.", |
| featurespec={"input_lines": "listsent", "summary_lines": "listsent", "evidence_labels":"listlistint"}, |
| label_column="evidence_labels", |
| ), |
| USBConfig( |
| name="multisentence_compression", |
| description="Given a list of sentences from an article, generate a single sentence summary of the presented cluster of sentences.", |
| featurespec={"summ_idx": "int", "input_lines": "listsent", "output_lines": "listsent"}, |
| label_column="output_lines", |
| ), |
| USBConfig( |
| name="extractive_summarization", |
| description="Given an article, generate an extractive summary by producing a subset o the article's sentences", |
| featurespec={"input_lines": "listsent", "labels": "listint"}, |
| label_column="labels", |
| ), |
| USBConfig( |
| name="abstractive_summarization", |
| description="Given an article, generate its abstractive summary", |
| featurespec={"input_lines": "listsent", "output_lines": "listsent"}, |
| label_column="output_lines", |
| ), |
| USBConfig( |
| name="factuality_classification", |
| 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.", |
| featurespec={"summ_idx": "int", "input_lines": "listsent", "summary_sent": "sent", "label":"int"}, |
| label_column="label", |
| ), |
| USBConfig( |
| name="all_annotations", |
| description="All annotations collected in the creation of USB dataset in one place.", |
| featurespec={}, |
| label_column=None, |
| ), |
| ] |
|
|
| def _split_generators(self, dl_manager): |
|
|
| data_root = dl_manager.download_and_extract(self.config.data_url) |
| |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "data_file": f"{data_root}/{self.config.name}/train.jsonl", |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "data_file": f"{data_root}/{self.config.name}/validation.jsonl", |
| "split": "validation", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "data_file": f"{data_root}/{self.config.name}/test.jsonl", |
| "split": "test", |
| }, |
| ), |
| ] |
| |
| def _generate_examples(self, data_file, split): |
| with jsonlines.open(data_file) as f: |
| for ex_idx,example in enumerate(f): |
| example["id"] = example["id"]+":"+str(ex_idx) |
| example["domain"] = example["id"].split("/")[0] |
| yield example["id"], example |
|
|
| def _info(self): |
| features = {} |
| features["id"] = datasets.Value("string") |
| features["domain"] = datasets.Value("string") |
|
|
| if self.config.name=="all_annotations": |
| |
| features["source"] = datasets.Sequence({"txt": datasets.Value("string"), "section_name": datasets.Value("string"), "section_index": datasets.Value("int32"), "is_header":datasets.Value("bool")}) |
| features["summary"] = datasets.Sequence({"pre_edit": datasets.Value("string"), "post_edit": datasets.Value("string"), "evidence": datasets.Sequence(datasets.Value("int32"))}) |
| |
| for (feature_name,dtype) in self.config.featurespec.items(): |
| hf_dtype = None |
| if dtype=="int": |
| hf_dtype = datasets.Value("int32") |
| elif dtype=="listint": |
| hf_dtype = datasets.Sequence(datasets.Value("int32")) |
| elif dtype=="listlistint": |
| hf_dtype = datasets.Sequence(datasets.Sequence(datasets.Value("int32"))) |
| elif dtype=="sent": |
| hf_dtype = datasets.Value("string") |
| elif dtype=="listsent": |
| hf_dtype = datasets.Sequence(datasets.Value("string")) |
| else: |
| raise NotImplementedError |
|
|
| features[feature_name] = hf_dtype |
|
|
| return datasets.DatasetInfo( |
| description=DESCRIPTION_BLOB, |
| features=datasets.Features(features), |
| homepage=self.config.url, |
| citation=self.config.citation, |
| ) |
