---
dataset_info:
  features:
  - name: summary
    dtype: string
  - name: article
    dtype: string
  splits:
  - name: train
    num_bytes: 98933510
    num_examples: 8000
  - name: validation
    num_bytes: 12217867
    num_examples: 1000
  - name: test
    num_bytes: 13227741
    num_examples: 1000
  download_size: 63278508
  dataset_size: 124379118
---
# Dataset Card for "HeSum"

> While large language models (LLMs) excel in various natural language tasks in English, their performance in lower-resourced languages like Hebrew, especially for generative tasks such as abstractive summarization, remains unclear. The high morphological richness in Hebrew adds further challenges due to the ambiguity in sentence comprehension and the complexities in meaning construction. In this paper, we address this resource and evaluation gap by introducing HeSum, a novel benchmark specifically designed for abstractive text summarization in Modern Hebrew. HeSum consists of 10,000 article-summary pairs sourced from Hebrew news websites written by professionals. Linguistic analysis confirms HeSum's high abstractness and unique morphological challenges. We show that HeSum presents distinct difficulties for contemporary state-of-the-art LLMs, establishing it as a valuable testbed for generative language technology in Hebrew, and MRLs generative challenges in general.

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## Citation

```bibtex
@misc{pazargaman2024hesum,
      title={HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew}, 
      author={Tzuf Paz-Argaman and Itai Mondshine and Asaf Achi Mordechai and Reut Tsarfaty},
      year={2024},
      eprint={2406.03897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```