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Attention expansion boosts keyphrase extraction from long documents

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种“注意力扩展”机制，以改进从长文档中提取关键词。该方法使用词嵌入将预训练语言模型（PLM）的表示与周围文本的信息相结合，有效地拓宽了模型的上下文范围，而无需进行全文档注意力或昂贵的LLM推理。在各种PLM骨干和数据集上的评估显示出一致的性能提升，确立了注意力扩展作为长文档KPE的有效策略。 AI

影响 提高了从长文本中检索信息的效率和有效性，可能改进研究和内容分析。

排序理由 这是一篇详细介绍关键词提取新方法的学术论文。

在 arXiv cs.AI 阅读 →

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Attention expansion boosts keyphrase extraction from long documents

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Roberto Mart\'inez-Cruz, Alvaro J. L\'opez-L\'opez, Jos\'e Portela ·

    注意力扩展：利用增强注意力上下文嵌入从长文档中提取关键短语

    arXiv:2606.10716v1 Announce Type: cross Abstract: Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich contextualized representations. However, long-document KPE remains challenging becaus…

  2. arXiv cs.AI TIER_1 English(EN) · José Portela ·

    注意力扩展：利用增强注意力上下文嵌入技术提升长文档关键短语提取能力

    Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich contextualized representations. However, long-document KPE remains challenging because salient keyphrase evidence may be scattered acro…

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