QTSumm Dataset
QTSumm is a query-focused table summarization dataset proposed in EMNLP 2023 paper QTSUMM: Query-Focused Summarization over Tabular Data. The original Github repository is 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
Reference
@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}
}
- Downloads last month
- 58
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.