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Error code: ConfigNamesError Exception: ImportError Message: To be able to use kundank/usb, you need to install the following dependency: jsonlines. Please install it using 'pip install jsonlines' for instance. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response config_names = get_dataset_config_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1880, in dataset_module_factory return HubDatasetModuleFactoryWithScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module local_imports = _download_additional_modules( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules raise ImportError( ImportError: To be able to use kundank/usb, you need to install the following dependency: jsonlines. Please install it using 'pip install jsonlines' for instance.
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USB: A Unified Summarization Benchmark Across Tasks and Domains
This benchmark contains labeled datasets for 8 text summarization based tasks given below. The labeled datasets are created by collecting manual annotations on top of Wikipedia articles from 6 different domains.
Task | Description | Code snippet |
---|---|---|
Extractive Summarization | Highlight important sentences in the source article | load_dataset("kundank/usb","extractive_summarization") |
Abstractive Summarization | Generate a summary of the source | load_dataset("kundank/usb","abstractive_summarization") |
Topic-based Summarization | Generate a summary of the source focusing on the given topic | load_dataset("kundank/usb","topicbased_summarization") |
Multi-sentence Compression | Compress selected sentences into a one-line summary | load_dataset("kundank/usb","multisentence_compression") |
Evidence Extraction | Surface evidence from the source for a summary sentence | load_dataset("kundank/usb","evidence_extraction") |
Factuality Classification | Predict the factual accuracy of a summary sentence with respect to provided evidence | load_dataset("kundank/usb","factuality_classification") |
Unsupported Span Prediction | Identify spans in a summary sentence which are not substantiated by the provided evidence | load_dataset("kundank/usb","unsupported_span_prediction") |
Fixing Factuality | Rewrite a summary sentence to remove any factual errors or unsupported claims, with respect to provided evidence | load_dataset("kundank/usb","fixing_factuality") |
Additionally, to load the full set of collected annotations which were leveraged to make the labeled datasets for above tasks, use the command: load_dataset("kundank/usb","all_annotations")
Trained models
We fine-tuned Flan-T5-XL models on the training set of each task in the benchmark. They are available at the links given below:
Task | Finetuned Flan-T5-XL model |
---|---|
Extractive Summarization | link |
Abstractive Summarization | link |
Topic-based Summarization | link |
Multi-sentence Compression | link |
Evidence Extraction | link |
Factuality Classification | link |
Unsupported Span Prediction | link |
Fixing Factuality | link |
More details can be found in the paper: https://aclanthology.org/2023.findings-emnlp.592/
If you use this dataset, please cite it as below:
@inproceedings{krishna-etal-2023-usb,
title = "{USB}: A Unified Summarization Benchmark Across Tasks and Domains",
author = "Krishna, Kundan and
Gupta, Prakhar and
Ramprasad, Sanjana and
Wallace, Byron and
Bigham, Jeffrey and
Lipton, Zachary",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
year = "2023",
pages = "8826--8845"
}
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