transformers pytorch bart text2text-generation table-to-text summarization long-form-question-answering en dataset:yale-nlp/QTSumm arxiv:2305.14303 license:mit autotrain_compatible endpoints_compatible region:us
---
datasets:
- yale-nlp/QTSumm
language: en
license: mit
tags:
- table-to-text
- summarization
- long-form-question-answering
---

# QTSumm Dataset

QTSumm is a query-focused table summarization dataset proposed in EMNLP 2023 paper [QTSUMM: Query-Focused Summarization over Tabular Data](https://arxiv.org/pdf/2305.14303.pdf). The original Github repository is [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm).

## Model Description

`yale-nlp/bart-large-finetuned-qtsumm` (based on BART architecture) is initialized with `facebook/bart-large` and finetuned on the QTSumm dataset.

## Usage

Check the github repository: [https://github.com/yale-nlp/QTSumm](https://github.com/yale-nlp/QTSumm)

## Reference

```bibtex
@misc{zhao2023qtsumm,
      title={QTSUMM: Query-Focused Summarization over Tabular Data}, 
      author={Yilun Zhao and Zhenting Qi and Linyong Nan and Boyu Mi and Yixin Liu and Weijin Zou and Simeng Han and Xiangru Tang and Yumo Xu and Arman Cohan and Dragomir Radev},
      year={2023},
      eprint={2305.14303},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```