{
  "id": 13066,
  "url": "https://arxiv.org/abs/2607.21010v1",
  "title": "Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers",
  "summary": "Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coherent and fluent summaries. However, underlying stochasticity of the large language models raises concerns about the stability and trustworthiness of the LLM-generated summaries. This issue has become increasingly important due to proliferation of LLM-generated summaries in educational settings, where students and researchers summarize complex academic material",
  "authors": "Vasudha Bhatnagar, Purnima Bindal, Vikas Kumar, Raj Kumari Bahl",
  "category": "research",
  "topics": "children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T07:48:20.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/13066",
  "original_url": "https://arxiv.org/abs/2607.21010v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}