adding data builder script

usb.py

@@ -0,0 +1,162 @@
+# Acknowledgement: dataset builder script adapted from https://huggingface.co/datasets/glue/blob/main/glue.py
+
+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,
+        text_features,
+        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.text_features = text_features
+        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",
+            text_features={"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",
+            text_features={"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.",
+            text_features={"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.",
+            text_features={"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.",
+            text_features={"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",
+            text_features={"input_lines": "listsent", "labels": "listint"},
+            label_column="labels",
+        ),
+        USBConfig(
+            name="abstractive_summarization",
+            description="Given an article, generate its abstractive summary",
+            text_features={"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.",
+            text_features={"summ_idx": "int", "input_lines": "listsent", "summary_sent": "sent", "label":"int"},
+            label_column="label",
+        ),
+    ]
+
+    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")
+        
+        for (text_feature,dtype) in self.config.text_features.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[text_feature] = hf_dtype
+
+        return datasets.DatasetInfo(
+            description=DESCRIPTION_BLOB,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation,
+        )