|
--- |
|
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" |
|
} |
|
``` |
|
|