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Abstract <p>We present TradeNewsSum—a corpus for abstractive summarization of international trade news—covering Russian- and English-language publications from domain-specific sources. All summaries are manually prepared following unified guidelines. We conducted experiments with fine-tuning transformer and seq2seq models and performed automatic evaluation using the LLM-as-a-judge scheme. LLaMA 3.1 in instruction-prompting mode achieved the best results, showing high scores across metrics, including factual completeness.</p>

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Abstractive Summarization for Trade News Analysis Based on a New Domain-Specific Dataset

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • D. A. Liutova,
  • V. A. Malykh

摘要

Abstract

We present TradeNewsSum—a corpus for abstractive summarization of international trade news—covering Russian- and English-language publications from domain-specific sources. All summaries are manually prepared following unified guidelines. We conducted experiments with fine-tuning transformer and seq2seq models and performed automatic evaluation using the LLM-as-a-judge scheme. LLaMA 3.1 in instruction-prompting mode achieved the best results, showing high scores across metrics, including factual completeness.