Summary of Hesum: a Novel Dataset For Abstractive Text Summarization in Hebrew, by Tzuf Paz-argaman et al.
HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew
by Tzuf Paz-Argaman, Itai Mondshine, Asaf Achi Mordechai, Reut Tsarfaty
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the performance of large language models (LLMs) on abstractive summarization tasks in Modern Hebrew, which remains unclear despite their excellence in natural language processing. The high morphological richness in Hebrew adds complexity due to ambiguous sentence comprehension and meaning construction. To address this resource and evaluation gap, the authors introduce HeSum, a novel benchmark for abstractive text summarization in Modern Hebrew, comprising 10,000 article-summary pairs from professional news websites. The paper highlights the challenges posed by HeSum, which are distinct from those faced by contemporary state-of-the-art LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well big language models do on a special kind of writing task in Hebrew, where they have to summarize articles into shorter texts. This is tricky because Hebrew has lots of words and meanings that can be confusing. The researchers create a new test set with 10,000 pairs of articles and summaries written by professionals, which shows how hard it is for language models to do this job well. This helps us understand what language models are good at and what they struggle with. |
Keywords
» Artificial intelligence » Natural language processing » Summarization
