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--- |
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license: apache-2.0 |
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task_categories: |
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- summarization |
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language: |
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- en |
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tags: |
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- factchecking |
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- summarization |
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- nli |
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size_categories: |
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- 1K<n<10K |
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--- |
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# USB: A Unified Summarization Benchmark Across Tasks and Domains |
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This benchmark contains labeled datasets for 8 text summarization based tasks given below. |
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The labeled datasets are created by collecting manual annotations on top of Wikipedia articles from 6 different domains. |
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|Task |Description |Code snippet | |
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|----------------|-------------------------------|-----------------------------| |
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| Extractive Summarization | Highlight important sentences in the source article | `load_dataset("kundank/usb","extractive_summarization")` | |
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| Abstractive Summarization | Generate a summary of the source | `load_dataset("kundank/usb","abstractive_summarization")` | |
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| Topic-based Summarization | Generate a summary of the source focusing on the given topic | `load_dataset("kundank/usb","topicbased_summarization")` | |
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| Multi-sentence Compression | Compress selected sentences into a one-line summary | `load_dataset("kundank/usb","multisentence_compression")` | |
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| Evidence Extraction | Surface evidence from the source for a summary sentence | `load_dataset("kundank/usb","evidence_extraction")` | |
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| Factuality Classification | Predict the factual accuracy of a summary sentence with respect to provided evidence | `load_dataset("kundank/usb","factuality_classification")` | |
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| Unsupported Span Prediction | Identify spans in a summary sentence which are not substantiated by the provided evidence | `load_dataset("kundank/usb","unsupported_span_prediction")` | |
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| 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")` | |
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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")`` |
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## Trained models |
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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: |
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|Task |Finetuned Flan-T5-XL model | |
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|----------------|-----------------------------| |
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| Extractive Summarization | [link](https://huggingface.co/kundank/usb-extractive_summarization-flant5xl) | |
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| Abstractive Summarization | [link](https://huggingface.co/kundank/usb-abstractive_summarization-flant5xl) | |
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| Topic-based Summarization | [link](https://huggingface.co/kundank/usb-topicbased_summarization-flant5xl) | |
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| Multi-sentence Compression | [link](https://huggingface.co/kundank/usb-multisentence_compression-flant5xl) | |
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| Evidence Extraction | [link](https://huggingface.co/kundank/usb-evidence_extraction-flant5xl) | |
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| Factuality Classification | [link](https://huggingface.co/kundank/usb-factuality_classification-flant5xl) | |
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| Unsupported Span Prediction | [link](https://huggingface.co/kundank/usb-unsupported_span_prediction-flant5xl) | |
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| Fixing Factuality | [link](https://huggingface.co/kundank/usb-fixing_factuality-flant5xl) | |
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More details can be found in the paper: https://aclanthology.org/2023.findings-emnlp.592/ |
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If you use this dataset, please cite it as below: |
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``` |
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@inproceedings{krishna-etal-2023-usb, |
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title = "{USB}: A Unified Summarization Benchmark Across Tasks and Domains", |
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author = "Krishna, Kundan and |
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Gupta, Prakhar and |
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Ramprasad, Sanjana and |
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Wallace, Byron and |
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Bigham, Jeffrey and |
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Lipton, Zachary", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023", |
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year = "2023", |
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pages = "8826--8845" |
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} |
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``` |
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