---
license: apache-2.0
task_categories:
- summarization
language:
- en
tags:
- factchecking
- summarization
- nli
size_categories:
- 1K<n<10K
---


# 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](https://huggingface.co/kundank/usb-extractive_summarization-flant5xl) |
| Abstractive Summarization | [link](https://huggingface.co/kundank/usb-abstractive_summarization-flant5xl) |
| Topic-based Summarization | [link](https://huggingface.co/kundank/usb-topicbased_summarization-flant5xl) |
| Multi-sentence Compression | [link](https://huggingface.co/kundank/usb-multisentence_compression-flant5xl) |
| Evidence Extraction | [link](https://huggingface.co/kundank/usb-evidence_extraction-flant5xl) |
| Factuality Classification | [link](https://huggingface.co/kundank/usb-factuality_classification-flant5xl) |
| Unsupported Span Prediction | [link](https://huggingface.co/kundank/usb-unsupported_span_prediction-flant5xl) |
| Fixing Factuality | [link](https://huggingface.co/kundank/usb-fixing_factuality-flant5xl) |




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"
}
```